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4575 lines
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Nubenetes V2 | The AI's Cut
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<a href="https://github.com/nubenetes/awesome-kubernetes" title="Go to repository" class="md-source" data-md-component="source">
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nubenetes/awesome-kubernetes
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<a href="https://nubenetes.com/v1/" class="md-tabs__link">
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🔙 Back to V1 (Exhaustive)
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</a>
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The 2026 Vision
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</a>
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Technical Tags
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Agentic Video Hub
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<li class="md-tabs__item md-tabs__item--active">
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<a href="../ai-agents-mcp/" class="md-tabs__link">
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AI
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</a>
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</li>
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<li class="md-tabs__item">
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<a href="../about/" class="md-tabs__link">
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Architectural Foundations
|
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</a>
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||
</li>
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<li class="md-tabs__item">
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<a href="../chaos-engineering/" class="md-tabs__link">
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Platform & Site Reliability
|
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</a>
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</li>
|
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<li class="md-tabs__item">
|
||
<a href="../ansible/" class="md-tabs__link">
|
||
|
||
|
||
|
||
|
||
|
||
Hardened Infrastructure
|
||
|
||
</a>
|
||
</li>
|
||
|
||
|
||
|
||
|
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|
||
|
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|
||
|
||
|
||
|
||
|
||
<li class="md-tabs__item">
|
||
<a href="../GoogleCloudPlatform/" class="md-tabs__link">
|
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|
||
|
||
|
||
|
||
|
||
Cloud Providers (Hyperscalers)
|
||
|
||
</a>
|
||
</li>
|
||
|
||
|
||
|
||
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<li class="md-tabs__item">
|
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<a href="../caching/" class="md-tabs__link">
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Networking & Service Mesh
|
||
|
||
</a>
|
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</li>
|
||
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||
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<li class="md-tabs__item">
|
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<a href="../container-managers/" class="md-tabs__link">
|
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|
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|
||
|
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|
||
The Container Stack
|
||
|
||
</a>
|
||
</li>
|
||
|
||
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<li class="md-tabs__item">
|
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<a href="../crunchydata/" class="md-tabs__link">
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Data & Advanced Analytics
|
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</a>
|
||
</li>
|
||
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<li class="md-tabs__item">
|
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<a href="../argo/" class="md-tabs__link">
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Engineering Pipeline
|
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||
</a>
|
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</li>
|
||
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||
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<li class="md-tabs__item">
|
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<a href="../ChromeDevTools/" class="md-tabs__link">
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Developer Ecosystem
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</a>
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</li>
|
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<li class="md-tabs__item">
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<a href="../appointment-scheduling/" class="md-tabs__link">
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Career & Industry
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</a>
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</ul>
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|
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|
||
|
||
<main class="md-main" data-md-component="main">
|
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<div class="md-main__inner md-grid">
|
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|
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|
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|
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<div class="md-sidebar md-sidebar--primary" data-md-component="sidebar" data-md-type="navigation" >
|
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<div class="md-sidebar__scrollwrap">
|
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<div class="md-sidebar__inner">
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|
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|
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|
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|
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|
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<nav class="md-nav md-nav--primary md-nav--lifted md-nav--integrated" aria-label="Navigation" data-md-level="0">
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<label class="md-nav__title" for="__drawer">
|
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<a href=".." title="Nubenetes V2 | The AI's Cut" class="md-nav__button md-logo" aria-label="Nubenetes V2 | The AI's Cut" data-md-component="logo">
|
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|
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|
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<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
|
||
|
||
</a>
|
||
Nubenetes V2 | The AI's Cut
|
||
</label>
|
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|
||
<div class="md-nav__source">
|
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<a href="https://github.com/nubenetes/awesome-kubernetes" title="Go to repository" class="md-source" data-md-component="source">
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<div class="md-source__icon md-icon">
|
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|
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<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 7.1.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2025 Fonticons, Inc.--><path d="M439.6 236.1 244 40.5c-5.4-5.5-12.8-8.5-20.4-8.5s-15 3-20.4 8.4L162.5 81l51.5 51.5c27.1-9.1 52.7 16.8 43.4 43.7l49.7 49.7c34.2-11.8 61.2 31 35.5 56.7-26.5 26.5-70.2-2.9-56-37.3L240.3 199v121.9c25.3 12.5 22.3 41.8 9.1 55-6.4 6.4-15.2 10.1-24.3 10.1s-17.8-3.6-24.3-10.1c-17.6-17.6-11.1-46.9 11.2-56v-123c-20.8-8.5-24.6-30.7-18.6-45L142.6 101 8.5 235.1C3 240.6 0 247.9 0 255.5s3 15 8.5 20.4l195.6 195.7c5.4 5.4 12.7 8.4 20.4 8.4s15-3 20.4-8.4l194.7-194.7c5.4-5.4 8.4-12.8 8.4-20.4s-3-15-8.4-20.4"/></svg>
|
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</div>
|
||
<div class="md-source__repository">
|
||
nubenetes/awesome-kubernetes
|
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</div>
|
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</a>
|
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</div>
|
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|
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<ul class="md-nav__list" data-md-scrollfix>
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<li class="md-nav__item">
|
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<a href="https://nubenetes.com/v1/" class="md-nav__link">
|
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|
||
|
||
|
||
<span class="md-ellipsis">
|
||
|
||
|
||
🔙 Back to V1 (Exhaustive)
|
||
|
||
|
||
|
||
</span>
|
||
|
||
|
||
|
||
</a>
|
||
</li>
|
||
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|
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|
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|
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|
||
|
||
|
||
|
||
|
||
<li class="md-nav__item">
|
||
<a href=".." class="md-nav__link">
|
||
|
||
|
||
|
||
<span class="md-ellipsis">
|
||
|
||
|
||
The 2026 Vision
|
||
|
||
|
||
|
||
</span>
|
||
|
||
|
||
|
||
</a>
|
||
</li>
|
||
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|
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|
||
|
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|
||
|
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|
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|
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|
||
<li class="md-nav__item">
|
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<a href="../tags/" class="md-nav__link">
|
||
|
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|
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|
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<span class="md-ellipsis">
|
||
|
||
|
||
Technical Tags
|
||
|
||
|
||
|
||
</span>
|
||
|
||
|
||
|
||
</a>
|
||
</li>
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
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|
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|
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|
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|
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|
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|
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|
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<li class="md-nav__item md-nav__item--pruned md-nav__item--nested">
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|
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<a href="../videos/" class="md-nav__link">
|
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|
||
|
||
|
||
<span class="md-ellipsis">
|
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|
||
|
||
Agentic Video Hub
|
||
|
||
|
||
|
||
</span>
|
||
|
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|
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|
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|
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<span class="md-nav__icon md-icon"></span>
|
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|
||
</a>
|
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|
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|
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|
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</li>
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
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|
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|
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|
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|
||
|
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|
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|
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|
||
<li class="md-nav__item md-nav__item--active md-nav__item--section md-nav__item--nested">
|
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|
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|
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<input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_5" checked>
|
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|
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|
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<label class="md-nav__link" for="__nav_5" id="__nav_5_label" tabindex="">
|
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|
||
|
||
|
||
<span class="md-ellipsis">
|
||
|
||
|
||
AI
|
||
|
||
|
||
|
||
</span>
|
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|
||
|
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|
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<span class="md-nav__icon md-icon"></span>
|
||
</label>
|
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|
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<nav class="md-nav" data-md-level="1" aria-labelledby="__nav_5_label" aria-expanded="true">
|
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<label class="md-nav__title" for="__nav_5">
|
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<span class="md-nav__icon md-icon"></span>
|
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|
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AI
|
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|
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|
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</label>
|
||
<ul class="md-nav__list" data-md-scrollfix>
|
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|
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|
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|
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|
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|
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|
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|
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<li class="md-nav__item">
|
||
<a href="../ai-agents-mcp/" class="md-nav__link">
|
||
|
||
|
||
|
||
<span class="md-ellipsis">
|
||
|
||
|
||
AI Agents MCP
|
||
|
||
|
||
|
||
</span>
|
||
|
||
|
||
|
||
</a>
|
||
</li>
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
<li class="md-nav__item">
|
||
<a href="../ai/" class="md-nav__link">
|
||
|
||
|
||
|
||
<span class="md-ellipsis">
|
||
|
||
|
||
AI
|
||
|
||
|
||
|
||
</span>
|
||
|
||
|
||
|
||
</a>
|
||
</li>
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
<li class="md-nav__item">
|
||
<a href="../chatgpt/" class="md-nav__link">
|
||
|
||
|
||
|
||
<span class="md-ellipsis">
|
||
|
||
|
||
ChatGPT
|
||
|
||
|
||
|
||
</span>
|
||
|
||
|
||
|
||
</a>
|
||
</li>
|
||
|
||
|
||
|
||
|
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|
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|
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|
||
|
||
|
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|
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|
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|
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<li class="md-nav__item md-nav__item--active">
|
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|
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<input class="md-nav__toggle md-toggle" type="checkbox" id="__toc">
|
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|
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|
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|
||
<label class="md-nav__link md-nav__link--active" for="__toc">
|
||
|
||
|
||
|
||
<span class="md-ellipsis">
|
||
|
||
|
||
MLOps
|
||
|
||
|
||
|
||
</span>
|
||
|
||
|
||
|
||
<span class="md-nav__icon md-icon"></span>
|
||
</label>
|
||
|
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<a href="./" class="md-nav__link md-nav__link--active">
|
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|
||
|
||
|
||
<span class="md-ellipsis">
|
||
|
||
|
||
MLOps
|
||
|
||
|
||
|
||
</span>
|
||
|
||
|
||
|
||
</a>
|
||
|
||
|
||
|
||
<nav class="md-nav md-nav--secondary" aria-label="Table of contents">
|
||
|
||
|
||
|
||
|
||
|
||
|
||
<label class="md-nav__title" for="__toc">
|
||
<span class="md-nav__icon md-icon"></span>
|
||
Table of contents
|
||
</label>
|
||
<ul class="md-nav__list" data-md-component="toc" data-md-scrollfix>
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#table-of-contents" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Table of Contents
|
||
|
||
</span>
|
||
</a>
|
||
|
||
</li>
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#ai-and-machine-learning" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
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AI and Machine Learning
|
||
|
||
</span>
|
||
</a>
|
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|
||
<nav class="md-nav" aria-label="AI and Machine Learning">
|
||
<ul class="md-nav__list">
|
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|
||
<li class="md-nav__item">
|
||
<a href="#bio-computing" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
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|
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Bio-Computing
|
||
|
||
</span>
|
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</a>
|
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|
||
<nav class="md-nav" aria-label="Bio-Computing">
|
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<ul class="md-nav__list">
|
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|
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<li class="md-nav__item">
|
||
<a href="#protein-folding" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Protein Folding
|
||
|
||
</span>
|
||
</a>
|
||
|
||
</li>
|
||
|
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</ul>
|
||
</nav>
|
||
|
||
</li>
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#foundations" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
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|
||
Foundations
|
||
|
||
</span>
|
||
</a>
|
||
|
||
<nav class="md-nav" aria-label="Foundations">
|
||
<ul class="md-nav__list">
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#decision-intelligence" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Decision Intelligence
|
||
|
||
</span>
|
||
</a>
|
||
|
||
</li>
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#neural-networks" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Neural Networks
|
||
|
||
</span>
|
||
</a>
|
||
|
||
</li>
|
||
|
||
</ul>
|
||
</nav>
|
||
|
||
</li>
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#open-source" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Open Source
|
||
|
||
</span>
|
||
</a>
|
||
|
||
<nav class="md-nav" aria-label="Open Source">
|
||
<ul class="md-nav__list">
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#landscape-evaluation" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Landscape Evaluation
|
||
|
||
</span>
|
||
</a>
|
||
|
||
</li>
|
||
|
||
</ul>
|
||
</nav>
|
||
|
||
</li>
|
||
|
||
</ul>
|
||
</nav>
|
||
|
||
</li>
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#architectural-foundations" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Architectural Foundations
|
||
|
||
</span>
|
||
</a>
|
||
|
||
<nav class="md-nav" aria-label="Architectural Foundations">
|
||
<ul class="md-nav__list">
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#kubernetes-tools" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Kubernetes Tools
|
||
|
||
</span>
|
||
</a>
|
||
|
||
<nav class="md-nav" aria-label="Kubernetes Tools">
|
||
<ul class="md-nav__list">
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#general-reference" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
General Reference
|
||
|
||
</span>
|
||
</a>
|
||
|
||
</li>
|
||
|
||
</ul>
|
||
</nav>
|
||
|
||
</li>
|
||
|
||
</ul>
|
||
</nav>
|
||
|
||
</li>
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#automl" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
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|
||
AutoML
|
||
|
||
</span>
|
||
</a>
|
||
|
||
<nav class="md-nav" aria-label="AutoML">
|
||
<ul class="md-nav__list">
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#low-code" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Low-Code
|
||
|
||
</span>
|
||
</a>
|
||
|
||
<nav class="md-nav" aria-label="Low-Code">
|
||
<ul class="md-nav__list">
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#code-generation" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Code Generation
|
||
|
||
</span>
|
||
</a>
|
||
|
||
</li>
|
||
|
||
</ul>
|
||
</nav>
|
||
|
||
</li>
|
||
|
||
</ul>
|
||
</nav>
|
||
|
||
</li>
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#ci-cd" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
CI-CD
|
||
|
||
</span>
|
||
</a>
|
||
|
||
<nav class="md-nav" aria-label="CI-CD">
|
||
<ul class="md-nav__list">
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#devops" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
DevOps
|
||
|
||
</span>
|
||
</a>
|
||
|
||
</li>
|
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|
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</ul>
|
||
</nav>
|
||
|
||
</li>
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#cicd" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
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|
||
CICD
|
||
|
||
</span>
|
||
</a>
|
||
|
||
<nav class="md-nav" aria-label="CICD">
|
||
<ul class="md-nav__list">
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#containers" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Containers
|
||
|
||
</span>
|
||
</a>
|
||
|
||
</li>
|
||
|
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</ul>
|
||
</nav>
|
||
|
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</li>
|
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|
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<li class="md-nav__item">
|
||
<a href="#cloud-platforms" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
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|
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Cloud Platforms
|
||
|
||
</span>
|
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</a>
|
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|
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<nav class="md-nav" aria-label="Cloud Platforms">
|
||
<ul class="md-nav__list">
|
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|
||
<li class="md-nav__item">
|
||
<a href="#aws" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
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|
||
AWS
|
||
|
||
</span>
|
||
</a>
|
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|
||
<nav class="md-nav" aria-label="AWS">
|
||
<ul class="md-nav__list">
|
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|
||
<li class="md-nav__item">
|
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<a href="#sagemaker" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
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|
||
SageMaker
|
||
|
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</span>
|
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</a>
|
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|
||
</li>
|
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|
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</ul>
|
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</nav>
|
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|
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</li>
|
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|
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<li class="md-nav__item">
|
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<a href="#azure" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Azure
|
||
|
||
</span>
|
||
</a>
|
||
|
||
<nav class="md-nav" aria-label="Azure">
|
||
<ul class="md-nav__list">
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#mlflow" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
MLflow
|
||
|
||
</span>
|
||
</a>
|
||
|
||
</li>
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#model-serving" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Model Serving
|
||
|
||
</span>
|
||
</a>
|
||
|
||
</li>
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#system-design" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
System Design
|
||
|
||
</span>
|
||
</a>
|
||
|
||
</li>
|
||
|
||
</ul>
|
||
</nav>
|
||
|
||
</li>
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#flyte-managed" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Flyte Managed
|
||
|
||
</span>
|
||
</a>
|
||
|
||
</li>
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#google-cloud" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Google Cloud
|
||
|
||
</span>
|
||
</a>
|
||
|
||
<nav class="md-nav" aria-label="Google Cloud">
|
||
<ul class="md-nav__list">
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#data-analytics" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Data Analytics
|
||
|
||
</span>
|
||
</a>
|
||
|
||
</li>
|
||
|
||
</ul>
|
||
</nav>
|
||
|
||
</li>
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#openshift" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
OpenShift
|
||
|
||
</span>
|
||
</a>
|
||
|
||
<nav class="md-nav" aria-label="OpenShift">
|
||
<ul class="md-nav__list">
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#enterprise-platforms" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Enterprise Platforms
|
||
|
||
</span>
|
||
</a>
|
||
|
||
</li>
|
||
|
||
</ul>
|
||
</nav>
|
||
|
||
</li>
|
||
|
||
</ul>
|
||
</nav>
|
||
|
||
</li>
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#data-engineering" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Data Engineering
|
||
|
||
</span>
|
||
</a>
|
||
|
||
<nav class="md-nav" aria-label="Data Engineering">
|
||
<ul class="md-nav__list">
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#data-labeling" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Data Labeling
|
||
|
||
</span>
|
||
</a>
|
||
|
||
<nav class="md-nav" aria-label="Data Labeling">
|
||
<ul class="md-nav__list">
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#human-in-the-loop" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Human-In-The-Loop
|
||
|
||
</span>
|
||
</a>
|
||
|
||
</li>
|
||
|
||
</ul>
|
||
</nav>
|
||
|
||
</li>
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#data-ops" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Data Ops
|
||
|
||
</span>
|
||
</a>
|
||
|
||
<nav class="md-nav" aria-label="Data Ops">
|
||
<ul class="md-nav__list">
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#ci-cd-1" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
CI-CD (1)
|
||
|
||
</span>
|
||
</a>
|
||
|
||
</li>
|
||
|
||
</ul>
|
||
</nav>
|
||
|
||
</li>
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#learning-roadmap" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Learning Roadmap
|
||
|
||
</span>
|
||
</a>
|
||
|
||
<nav class="md-nav" aria-label="Learning Roadmap">
|
||
<ul class="md-nav__list">
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#machine-learning" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Machine Learning
|
||
|
||
</span>
|
||
</a>
|
||
|
||
</li>
|
||
|
||
</ul>
|
||
</nav>
|
||
|
||
</li>
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#streaming" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Streaming
|
||
|
||
</span>
|
||
</a>
|
||
|
||
<nav class="md-nav" aria-label="Streaming">
|
||
<ul class="md-nav__list">
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#kafka" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Kafka
|
||
|
||
</span>
|
||
</a>
|
||
|
||
</li>
|
||
|
||
</ul>
|
||
</nav>
|
||
|
||
</li>
|
||
|
||
</ul>
|
||
</nav>
|
||
|
||
</li>
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#data-science" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Data Science
|
||
|
||
</span>
|
||
</a>
|
||
|
||
<nav class="md-nav" aria-label="Data Science">
|
||
<ul class="md-nav__list">
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#career-guidance" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Career Guidance
|
||
|
||
</span>
|
||
</a>
|
||
|
||
<nav class="md-nav" aria-label="Career Guidance">
|
||
<ul class="md-nav__list">
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#industry-trends" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Industry Trends
|
||
|
||
</span>
|
||
</a>
|
||
|
||
</li>
|
||
|
||
</ul>
|
||
</nav>
|
||
|
||
</li>
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#cloud-notebooks" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Cloud Notebooks
|
||
|
||
</span>
|
||
</a>
|
||
|
||
<nav class="md-nav" aria-label="Cloud Notebooks">
|
||
<ul class="md-nav__list">
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#data-engineering-1" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Data Engineering (1)
|
||
|
||
</span>
|
||
</a>
|
||
|
||
</li>
|
||
|
||
</ul>
|
||
</nav>
|
||
|
||
</li>
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#data-frames" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Data Frames
|
||
|
||
</span>
|
||
</a>
|
||
|
||
<nav class="md-nav" aria-label="Data Frames">
|
||
<ul class="md-nav__list">
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#out-of-core-processing" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Out-of-Core Processing
|
||
|
||
</span>
|
||
</a>
|
||
|
||
</li>
|
||
|
||
</ul>
|
||
</nav>
|
||
|
||
</li>
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#datasets" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Datasets
|
||
|
||
</span>
|
||
</a>
|
||
|
||
<nav class="md-nav" aria-label="Datasets">
|
||
<ul class="md-nav__list">
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#medical-imaging" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Medical Imaging
|
||
|
||
</span>
|
||
</a>
|
||
|
||
</li>
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#regression-analysis" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Regression Analysis
|
||
|
||
</span>
|
||
</a>
|
||
|
||
</li>
|
||
|
||
</ul>
|
||
</nav>
|
||
|
||
</li>
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#developer-tooling" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Developer Tooling
|
||
|
||
</span>
|
||
</a>
|
||
|
||
<nav class="md-nav" aria-label="Developer Tooling">
|
||
<ul class="md-nav__list">
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#python-ecosystem" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Python Ecosystem
|
||
|
||
</span>
|
||
</a>
|
||
|
||
</li>
|
||
|
||
</ul>
|
||
</nav>
|
||
|
||
</li>
|
||
|
||
</ul>
|
||
</nav>
|
||
|
||
</li>
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#data-version-control" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Data Version Control
|
||
|
||
</span>
|
||
</a>
|
||
|
||
<nav class="md-nav" aria-label="Data Version Control">
|
||
<ul class="md-nav__list">
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#developer-tooling-1" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Developer Tooling (1)
|
||
|
||
</span>
|
||
</a>
|
||
|
||
</li>
|
||
|
||
</ul>
|
||
</nav>
|
||
|
||
</li>
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#deployment" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Deployment
|
||
|
||
</span>
|
||
</a>
|
||
|
||
<nav class="md-nav" aria-label="Deployment">
|
||
<ul class="md-nav__list">
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#kubernetes-orchestration" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Kubernetes Orchestration
|
||
|
||
</span>
|
||
</a>
|
||
|
||
</li>
|
||
|
||
</ul>
|
||
</nav>
|
||
|
||
</li>
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#development-environments" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Development Environments
|
||
|
||
</span>
|
||
</a>
|
||
|
||
<nav class="md-nav" aria-label="Development Environments">
|
||
<ul class="md-nav__list">
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#containerization" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Containerization
|
||
|
||
</span>
|
||
</a>
|
||
|
||
</li>
|
||
|
||
</ul>
|
||
</nav>
|
||
|
||
</li>
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#distributed-systems" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Distributed Systems
|
||
|
||
</span>
|
||
</a>
|
||
|
||
<nav class="md-nav" aria-label="Distributed Systems">
|
||
<ul class="md-nav__list">
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#compute-engines" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Compute Engines
|
||
|
||
</span>
|
||
</a>
|
||
|
||
<nav class="md-nav" aria-label="Compute Engines">
|
||
<ul class="md-nav__list">
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#ray" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Ray
|
||
|
||
</span>
|
||
</a>
|
||
|
||
</li>
|
||
|
||
</ul>
|
||
</nav>
|
||
|
||
</li>
|
||
|
||
</ul>
|
||
</nav>
|
||
|
||
</li>
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#experiment-tracking" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Experiment Tracking
|
||
|
||
</span>
|
||
</a>
|
||
|
||
<nav class="md-nav" aria-label="Experiment Tracking">
|
||
<ul class="md-nav__list">
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#visualization" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Visualization
|
||
|
||
</span>
|
||
</a>
|
||
|
||
</li>
|
||
|
||
</ul>
|
||
</nav>
|
||
|
||
</li>
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#generative-ai" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Generative AI
|
||
|
||
</span>
|
||
</a>
|
||
|
||
<nav class="md-nav" aria-label="Generative AI">
|
||
<ul class="md-nav__list">
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#distributed-systems-1" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Distributed Systems (1)
|
||
|
||
</span>
|
||
</a>
|
||
|
||
<nav class="md-nav" aria-label="Distributed Systems (1)">
|
||
<ul class="md-nav__list">
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#case-studies" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Case Studies
|
||
|
||
</span>
|
||
</a>
|
||
|
||
</li>
|
||
|
||
</ul>
|
||
</nav>
|
||
|
||
</li>
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#llm-ops" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
LLM Ops
|
||
|
||
</span>
|
||
</a>
|
||
|
||
<nav class="md-nav" aria-label="LLM Ops">
|
||
<ul class="md-nav__list">
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#aws-1" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
AWS (1)
|
||
|
||
</span>
|
||
</a>
|
||
|
||
</li>
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#self-assessment" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Self-Assessment
|
||
|
||
</span>
|
||
</a>
|
||
|
||
</li>
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#system-design-1" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
System Design (1)
|
||
|
||
</span>
|
||
</a>
|
||
|
||
</li>
|
||
|
||
</ul>
|
||
</nav>
|
||
|
||
</li>
|
||
|
||
</ul>
|
||
</nav>
|
||
|
||
</li>
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#governance" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Governance
|
||
|
||
</span>
|
||
</a>
|
||
|
||
<nav class="md-nav" aria-label="Governance">
|
||
<ul class="md-nav__list">
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#maturity-models" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Maturity Models
|
||
|
||
</span>
|
||
</a>
|
||
|
||
</li>
|
||
|
||
</ul>
|
||
</nav>
|
||
|
||
</li>
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#infrastructure" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Infrastructure
|
||
|
||
</span>
|
||
</a>
|
||
|
||
<nav class="md-nav" aria-label="Infrastructure">
|
||
<ul class="md-nav__list">
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#container-runtimes" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Container Runtimes
|
||
|
||
</span>
|
||
</a>
|
||
|
||
<nav class="md-nav" aria-label="Container Runtimes">
|
||
<ul class="md-nav__list">
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#gpu-drivers" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
GPU Drivers
|
||
|
||
</span>
|
||
</a>
|
||
|
||
</li>
|
||
|
||
</ul>
|
||
</nav>
|
||
|
||
</li>
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#gpu-monitoring" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
GPU Monitoring
|
||
|
||
</span>
|
||
</a>
|
||
|
||
<nav class="md-nav" aria-label="GPU Monitoring">
|
||
<ul class="md-nav__list">
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#command-line-tools" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Command Line Tools
|
||
|
||
</span>
|
||
</a>
|
||
|
||
</li>
|
||
|
||
</ul>
|
||
</nav>
|
||
|
||
</li>
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#gpu-orchestration" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
GPU Orchestration
|
||
|
||
</span>
|
||
</a>
|
||
|
||
<nav class="md-nav" aria-label="GPU Orchestration">
|
||
<ul class="md-nav__list">
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#kubernetes-operators" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Kubernetes Operators
|
||
|
||
</span>
|
||
</a>
|
||
|
||
</li>
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#kubernetes-setup" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Kubernetes Setup
|
||
|
||
</span>
|
||
</a>
|
||
|
||
</li>
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#nix-package-manager" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Nix Package Manager
|
||
|
||
</span>
|
||
</a>
|
||
|
||
</li>
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#resource-allocation" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Resource Allocation
|
||
|
||
</span>
|
||
</a>
|
||
|
||
</li>
|
||
|
||
</ul>
|
||
</nav>
|
||
|
||
</li>
|
||
|
||
</ul>
|
||
</nav>
|
||
|
||
</li>
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#kubernetes" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Kubernetes
|
||
|
||
</span>
|
||
</a>
|
||
|
||
<nav class="md-nav" aria-label="Kubernetes">
|
||
<ul class="md-nav__list">
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#architectural-patterns" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Architectural Patterns
|
||
|
||
</span>
|
||
</a>
|
||
|
||
</li>
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#artifact-registration" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Artifact Registration
|
||
|
||
</span>
|
||
</a>
|
||
|
||
</li>
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#component-engineering" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Component Engineering
|
||
|
||
</span>
|
||
</a>
|
||
|
||
</li>
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#deployment-guides" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Deployment Guides
|
||
|
||
</span>
|
||
</a>
|
||
|
||
</li>
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#kubeflow" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Kubeflow
|
||
|
||
</span>
|
||
</a>
|
||
|
||
</li>
|
||
|
||
</ul>
|
||
</nav>
|
||
|
||
</li>
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#learning-roadmap-1" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Learning Roadmap (1)
|
||
|
||
</span>
|
||
</a>
|
||
|
||
<nav class="md-nav" aria-label="Learning Roadmap (1)">
|
||
<ul class="md-nav__list">
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#courses" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Courses
|
||
|
||
</span>
|
||
</a>
|
||
|
||
</li>
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#curated-reference" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Curated Reference
|
||
|
||
</span>
|
||
</a>
|
||
|
||
</li>
|
||
|
||
</ul>
|
||
</nav>
|
||
|
||
</li>
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#machine-learning-1" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Machine Learning (1)
|
||
|
||
</span>
|
||
</a>
|
||
|
||
<nav class="md-nav" aria-label="Machine Learning (1)">
|
||
<ul class="md-nav__list">
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#competitions" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Competitions
|
||
|
||
</span>
|
||
</a>
|
||
|
||
<nav class="md-nav" aria-label="Competitions">
|
||
<ul class="md-nav__list">
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#datasets-1" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Datasets (1)
|
||
|
||
</span>
|
||
</a>
|
||
|
||
</li>
|
||
|
||
</ul>
|
||
</nav>
|
||
|
||
</li>
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#computer-vision" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Computer Vision
|
||
|
||
</span>
|
||
</a>
|
||
|
||
<nav class="md-nav" aria-label="Computer Vision">
|
||
<ul class="md-nav__list">
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#instance-segmentation" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Instance Segmentation
|
||
|
||
</span>
|
||
</a>
|
||
|
||
</li>
|
||
|
||
</ul>
|
||
</nav>
|
||
|
||
</li>
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#databases" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Databases
|
||
|
||
</span>
|
||
</a>
|
||
|
||
<nav class="md-nav" aria-label="Databases">
|
||
<ul class="md-nav__list">
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#in-database-ml" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
In-database ML
|
||
|
||
</span>
|
||
</a>
|
||
|
||
</li>
|
||
|
||
</ul>
|
||
</nav>
|
||
|
||
</li>
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#distributed-training" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Distributed Training
|
||
|
||
</span>
|
||
</a>
|
||
|
||
<nav class="md-nav" aria-label="Distributed Training">
|
||
<ul class="md-nav__list">
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#fault-tolerance" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Fault Tolerance
|
||
|
||
</span>
|
||
</a>
|
||
|
||
</li>
|
||
|
||
</ul>
|
||
</nav>
|
||
|
||
</li>
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#document-analysis" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Document Analysis
|
||
|
||
</span>
|
||
</a>
|
||
|
||
<nav class="md-nav" aria-label="Document Analysis">
|
||
<ul class="md-nav__list">
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#ocr" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
OCR
|
||
|
||
</span>
|
||
</a>
|
||
|
||
</li>
|
||
|
||
</ul>
|
||
</nav>
|
||
|
||
</li>
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#education" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Education
|
||
|
||
</span>
|
||
</a>
|
||
|
||
<nav class="md-nav" aria-label="Education">
|
||
<ul class="md-nav__list">
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#study-materials" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Study Materials
|
||
|
||
</span>
|
||
</a>
|
||
|
||
</li>
|
||
|
||
</ul>
|
||
</nav>
|
||
|
||
</li>
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#foundations-1" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Foundations (1)
|
||
|
||
</span>
|
||
</a>
|
||
|
||
<nav class="md-nav" aria-label="Foundations (1)">
|
||
<ul class="md-nav__list">
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#scratch-implementations" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Scratch Implementations
|
||
|
||
</span>
|
||
</a>
|
||
|
||
</li>
|
||
|
||
</ul>
|
||
</nav>
|
||
|
||
</li>
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#information-retrieval" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Information Retrieval
|
||
|
||
</span>
|
||
</a>
|
||
|
||
<nav class="md-nav" aria-label="Information Retrieval">
|
||
<ul class="md-nav__list">
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#rag-pipelines" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
RAG Pipelines
|
||
|
||
</span>
|
||
</a>
|
||
|
||
</li>
|
||
|
||
</ul>
|
||
</nav>
|
||
|
||
</li>
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#large-language-models" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Large Language Models
|
||
|
||
</span>
|
||
</a>
|
||
|
||
<nav class="md-nav" aria-label="Large Language Models">
|
||
<ul class="md-nav__list">
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#fine-tuning" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Fine-tuning
|
||
|
||
</span>
|
||
</a>
|
||
|
||
</li>
|
||
|
||
</ul>
|
||
</nav>
|
||
|
||
</li>
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#mlops" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
MLOps
|
||
|
||
</span>
|
||
</a>
|
||
|
||
<nav class="md-nav" aria-label="MLOps">
|
||
<ul class="md-nav__list">
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#model-pipelines" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Model Pipelines
|
||
|
||
</span>
|
||
</a>
|
||
|
||
</li>
|
||
|
||
</ul>
|
||
</nav>
|
||
|
||
</li>
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#medical-imaging-1" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Medical Imaging (1)
|
||
|
||
</span>
|
||
</a>
|
||
|
||
<nav class="md-nav" aria-label="Medical Imaging (1)">
|
||
<ul class="md-nav__list">
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#computer-vision-1" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Computer Vision (1)
|
||
|
||
</span>
|
||
</a>
|
||
|
||
</li>
|
||
|
||
</ul>
|
||
</nav>
|
||
|
||
</li>
|
||
|
||
</ul>
|
||
</nav>
|
||
|
||
</li>
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#medical-imaging-2" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Medical Imaging (2)
|
||
|
||
</span>
|
||
</a>
|
||
|
||
<nav class="md-nav" aria-label="Medical Imaging (2)">
|
||
<ul class="md-nav__list">
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#end-to-end-pipeline" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
End-to-End Pipeline
|
||
|
||
</span>
|
||
</a>
|
||
|
||
</li>
|
||
|
||
</ul>
|
||
</nav>
|
||
|
||
</li>
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#model-life-cycle" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Model Life Cycle
|
||
|
||
</span>
|
||
</a>
|
||
|
||
<nav class="md-nav" aria-label="Model Life Cycle">
|
||
<ul class="md-nav__list">
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#aws-2" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
AWS (2)
|
||
|
||
</span>
|
||
</a>
|
||
|
||
</li>
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#enterprise-patterns" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Enterprise Patterns
|
||
|
||
</span>
|
||
</a>
|
||
|
||
</li>
|
||
|
||
</ul>
|
||
</nav>
|
||
|
||
</li>
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#model-serving-1" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Model Serving (1)
|
||
|
||
</span>
|
||
</a>
|
||
|
||
<nav class="md-nav" aria-label="Model Serving (1)">
|
||
<ul class="md-nav__list">
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#api-development" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
API Development
|
||
|
||
</span>
|
||
</a>
|
||
|
||
<nav class="md-nav" aria-label="API Development">
|
||
<ul class="md-nav__list">
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#fastapi" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
FastAPI
|
||
|
||
</span>
|
||
</a>
|
||
|
||
</li>
|
||
|
||
</ul>
|
||
</nav>
|
||
|
||
</li>
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#architectural-patterns-1" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Architectural Patterns (1)
|
||
|
||
</span>
|
||
</a>
|
||
|
||
<nav class="md-nav" aria-label="Architectural Patterns (1)">
|
||
<ul class="md-nav__list">
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#infrastructure-selection" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Infrastructure Selection
|
||
|
||
</span>
|
||
</a>
|
||
|
||
</li>
|
||
|
||
</ul>
|
||
</nav>
|
||
|
||
</li>
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#kubernetes-1" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Kubernetes (1)
|
||
|
||
</span>
|
||
</a>
|
||
|
||
<nav class="md-nav" aria-label="Kubernetes (1)">
|
||
<ul class="md-nav__list">
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#kserve" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
KServe
|
||
|
||
</span>
|
||
</a>
|
||
|
||
</li>
|
||
|
||
</ul>
|
||
</nav>
|
||
|
||
</li>
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#microservices" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Microservices
|
||
|
||
</span>
|
||
</a>
|
||
|
||
</li>
|
||
|
||
</ul>
|
||
</nav>
|
||
|
||
</li>
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#orchestration" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Orchestration
|
||
|
||
</span>
|
||
</a>
|
||
|
||
<nav class="md-nav" aria-label="Orchestration">
|
||
<ul class="md-nav__list">
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#airflow" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Airflow
|
||
|
||
</span>
|
||
</a>
|
||
|
||
</li>
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#airflow-providers" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Airflow Providers
|
||
|
||
</span>
|
||
</a>
|
||
|
||
</li>
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#comparative-analysis" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Comparative Analysis
|
||
|
||
</span>
|
||
</a>
|
||
|
||
</li>
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#flyte" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Flyte
|
||
|
||
</span>
|
||
</a>
|
||
|
||
</li>
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#frameworks" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Frameworks
|
||
|
||
</span>
|
||
</a>
|
||
|
||
</li>
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#workflows" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Workflows
|
||
|
||
</span>
|
||
</a>
|
||
|
||
</li>
|
||
|
||
</ul>
|
||
</nav>
|
||
|
||
</li>
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#software-engineering" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Software Engineering
|
||
|
||
</span>
|
||
</a>
|
||
|
||
<nav class="md-nav" aria-label="Software Engineering">
|
||
<ul class="md-nav__list">
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#devops-1" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
DevOps (1)
|
||
|
||
</span>
|
||
</a>
|
||
|
||
<nav class="md-nav" aria-label="DevOps (1)">
|
||
<ul class="md-nav__list">
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#ai-assisted-workflows" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
AI-Assisted Workflows
|
||
|
||
</span>
|
||
</a>
|
||
|
||
</li>
|
||
|
||
</ul>
|
||
</nav>
|
||
|
||
</li>
|
||
|
||
</ul>
|
||
</nav>
|
||
|
||
</li>
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#workflow-transition" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Workflow Transition
|
||
|
||
</span>
|
||
</a>
|
||
|
||
<nav class="md-nav" aria-label="Workflow Transition">
|
||
<ul class="md-nav__list">
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#best-practices" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Best Practices
|
||
|
||
</span>
|
||
</a>
|
||
|
||
</li>
|
||
|
||
</ul>
|
||
</nav>
|
||
|
||
</li>
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#workshops" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Workshops
|
||
|
||
</span>
|
||
</a>
|
||
|
||
<nav class="md-nav" aria-label="Workshops">
|
||
<ul class="md-nav__list">
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#infrastructure-1" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Infrastructure (1)
|
||
|
||
</span>
|
||
</a>
|
||
|
||
</li>
|
||
|
||
</ul>
|
||
</nav>
|
||
|
||
</li>
|
||
|
||
</ul>
|
||
|
||
</nav>
|
||
|
||
</li>
|
||
|
||
|
||
|
||
|
||
</ul>
|
||
</nav>
|
||
|
||
</li>
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
<li class="md-nav__item md-nav__item--pruned md-nav__item--nested">
|
||
|
||
|
||
|
||
|
||
|
||
<a href="../about/" class="md-nav__link">
|
||
|
||
|
||
|
||
<span class="md-ellipsis">
|
||
|
||
|
||
Architectural Foundations
|
||
|
||
|
||
|
||
</span>
|
||
|
||
|
||
|
||
|
||
<span class="md-nav__icon md-icon"></span>
|
||
|
||
</a>
|
||
|
||
|
||
|
||
</li>
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
<li class="md-nav__item md-nav__item--pruned md-nav__item--nested">
|
||
|
||
|
||
|
||
|
||
|
||
<a href="../chaos-engineering/" class="md-nav__link">
|
||
|
||
|
||
|
||
<span class="md-ellipsis">
|
||
|
||
|
||
Platform & Site Reliability
|
||
|
||
|
||
|
||
</span>
|
||
|
||
|
||
|
||
|
||
<span class="md-nav__icon md-icon"></span>
|
||
|
||
</a>
|
||
|
||
|
||
|
||
</li>
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
<li class="md-nav__item md-nav__item--pruned md-nav__item--nested">
|
||
|
||
|
||
|
||
|
||
|
||
<a href="../ansible/" class="md-nav__link">
|
||
|
||
|
||
|
||
<span class="md-ellipsis">
|
||
|
||
|
||
Hardened Infrastructure
|
||
|
||
|
||
|
||
</span>
|
||
|
||
|
||
|
||
|
||
<span class="md-nav__icon md-icon"></span>
|
||
|
||
</a>
|
||
|
||
|
||
|
||
</li>
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
<li class="md-nav__item md-nav__item--pruned md-nav__item--nested">
|
||
|
||
|
||
|
||
|
||
|
||
<a href="../GoogleCloudPlatform/" class="md-nav__link">
|
||
|
||
|
||
|
||
<span class="md-ellipsis">
|
||
|
||
|
||
Cloud Providers (Hyperscalers)
|
||
|
||
|
||
|
||
</span>
|
||
|
||
|
||
|
||
|
||
<span class="md-nav__icon md-icon"></span>
|
||
|
||
</a>
|
||
|
||
|
||
|
||
</li>
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
<li class="md-nav__item md-nav__item--pruned md-nav__item--nested">
|
||
|
||
|
||
|
||
|
||
|
||
<a href="../caching/" class="md-nav__link">
|
||
|
||
|
||
|
||
<span class="md-ellipsis">
|
||
|
||
|
||
Networking & Service Mesh
|
||
|
||
|
||
|
||
</span>
|
||
|
||
|
||
|
||
|
||
<span class="md-nav__icon md-icon"></span>
|
||
|
||
</a>
|
||
|
||
|
||
|
||
</li>
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
<li class="md-nav__item md-nav__item--pruned md-nav__item--nested">
|
||
|
||
|
||
|
||
|
||
|
||
<a href="../container-managers/" class="md-nav__link">
|
||
|
||
|
||
|
||
<span class="md-ellipsis">
|
||
|
||
|
||
The Container Stack
|
||
|
||
|
||
|
||
</span>
|
||
|
||
|
||
|
||
|
||
<span class="md-nav__icon md-icon"></span>
|
||
|
||
</a>
|
||
|
||
|
||
|
||
</li>
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
<li class="md-nav__item md-nav__item--pruned md-nav__item--nested">
|
||
|
||
|
||
|
||
|
||
|
||
<a href="../crunchydata/" class="md-nav__link">
|
||
|
||
|
||
|
||
<span class="md-ellipsis">
|
||
|
||
|
||
Data & Advanced Analytics
|
||
|
||
|
||
|
||
</span>
|
||
|
||
|
||
|
||
|
||
<span class="md-nav__icon md-icon"></span>
|
||
|
||
</a>
|
||
|
||
|
||
|
||
</li>
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
<li class="md-nav__item md-nav__item--pruned md-nav__item--nested">
|
||
|
||
|
||
|
||
|
||
|
||
<a href="../argo/" class="md-nav__link">
|
||
|
||
|
||
|
||
<span class="md-ellipsis">
|
||
|
||
|
||
Engineering Pipeline
|
||
|
||
|
||
|
||
</span>
|
||
|
||
|
||
|
||
|
||
<span class="md-nav__icon md-icon"></span>
|
||
|
||
</a>
|
||
|
||
|
||
|
||
</li>
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
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||
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||
|
||
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|
||
<li class="md-nav__item md-nav__item--pruned md-nav__item--nested">
|
||
|
||
|
||
|
||
|
||
|
||
<a href="../ChromeDevTools/" class="md-nav__link">
|
||
|
||
|
||
|
||
<span class="md-ellipsis">
|
||
|
||
|
||
Developer Ecosystem
|
||
|
||
|
||
|
||
</span>
|
||
|
||
|
||
|
||
|
||
<span class="md-nav__icon md-icon"></span>
|
||
|
||
</a>
|
||
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||
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|
||
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|
||
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||
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||
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||
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||
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||
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<li class="md-nav__item md-nav__item--pruned md-nav__item--nested">
|
||
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||
|
||
|
||
|
||
|
||
<a href="../appointment-scheduling/" class="md-nav__link">
|
||
|
||
|
||
|
||
<span class="md-ellipsis">
|
||
|
||
|
||
Career & Industry
|
||
|
||
|
||
|
||
</span>
|
||
|
||
|
||
|
||
|
||
<span class="md-nav__icon md-icon"></span>
|
||
|
||
</a>
|
||
|
||
|
||
|
||
</li>
|
||
|
||
|
||
|
||
</ul>
|
||
</nav>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
|
||
|
||
|
||
|
||
<div class="md-content" data-md-component="content">
|
||
|
||
<article class="md-content__inner md-typeset">
|
||
|
||
|
||
|
||
|
||
|
||
<a href="https://github.com/nubenetes/awesome-kubernetes/edit/master/v2-docs/mlops.md" title="Edit this page" class="md-content__button md-icon" rel="edit">
|
||
|
||
<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M10 20H6V4h7v5h5v3.1l2-2V8l-6-6H6c-1.1 0-2 .9-2 2v16c0 1.1.9 2 2 2h4zm10.2-7c.1 0 .3.1.4.2l1.3 1.3c.2.2.2.6 0 .8l-1 1-2.1-2.1 1-1c.1-.1.2-.2.4-.2m0 3.9L14.1 23H12v-2.1l6.1-6.1z"/></svg>
|
||
</a>
|
||
|
||
|
||
|
||
|
||
|
||
<a href="https://github.com/nubenetes/awesome-kubernetes/raw/master/v2-docs/mlops.md" title="View source of this page" class="md-content__button md-icon">
|
||
|
||
<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M17 18c.56 0 1 .44 1 1s-.44 1-1 1-1-.44-1-1 .44-1 1-1m0-3c-2.73 0-5.06 1.66-6 4 .94 2.34 3.27 4 6 4s5.06-1.66 6-4c-.94-2.34-3.27-4-6-4m0 6.5a2.5 2.5 0 0 1-2.5-2.5 2.5 2.5 0 0 1 2.5-2.5 2.5 2.5 0 0 1 2.5 2.5 2.5 2.5 0 0 1-2.5 2.5M9.27 20H6V4h7v5h5v4.07c.7.08 1.36.25 2 .49V8l-6-6H6a2 2 0 0 0-2 2v16a2 2 0 0 0 2 2h4.5a8.2 8.2 0 0 1-1.23-2"/></svg>
|
||
</a>
|
||
|
||
|
||
|
||
<h1 id="machine-learning-ops-mlops-and-data-science">Machine Learning Ops (MLOps) and Data Science<a class="headerlink" href="#machine-learning-ops-mlops-and-data-science" title="Permanent link">¶</a></h1>
|
||
<div class="admonition tip">
|
||
<p class="admonition-title">Nubenetes V2 Elite Portal</p>
|
||
<p>You are browsing the AI-Curated V2 Elite Edition. Looking for the exhaustive list of references? Check out the <a href="/v1/mlops/"><strong>V1 Historical Archive</strong></a>.</p>
|
||
</div>
|
||
<div class="admonition info">
|
||
<p class="admonition-title">Architectural Context</p>
|
||
<p>Detailed reference for Machine Learning Ops (MLOps) and Data Science in the context of AI.</p>
|
||
</div>
|
||
<h2 id="table-of-contents">Table of Contents<a class="headerlink" href="#table-of-contents" title="Permanent link">¶</a></h2>
|
||
<ol>
|
||
<li><a href="#ai-and-machine-learning">AI and Machine Learning</a></li>
|
||
<li><a href="#bio-computing">Bio-Computing</a><ul>
|
||
<li><a href="#protein-folding">Protein Folding</a></li>
|
||
</ul>
|
||
</li>
|
||
<li><a href="#foundations">Foundations</a><ul>
|
||
<li><a href="#decision-intelligence">Decision Intelligence</a></li>
|
||
<li><a href="#neural-networks">Neural Networks</a></li>
|
||
</ul>
|
||
</li>
|
||
<li><a href="#open-source">Open Source</a><ul>
|
||
<li><a href="#landscape-evaluation">Landscape Evaluation</a></li>
|
||
</ul>
|
||
</li>
|
||
<li><a href="#architectural-foundations">Architectural Foundations</a></li>
|
||
<li><a href="#kubernetes-tools">Kubernetes Tools</a><ul>
|
||
<li><a href="#general-reference">General Reference</a></li>
|
||
</ul>
|
||
</li>
|
||
<li><a href="#automl">AutoML</a></li>
|
||
<li><a href="#low-code">Low-Code</a><ul>
|
||
<li><a href="#code-generation">Code Generation</a></li>
|
||
</ul>
|
||
</li>
|
||
<li><a href="#ci-cd">CI-CD</a></li>
|
||
<li><a href="#devops">DevOps</a></li>
|
||
<li><a href="#cicd">CICD</a></li>
|
||
<li><a href="#containers">Containers</a></li>
|
||
<li><a href="#cloud-platforms">Cloud Platforms</a></li>
|
||
<li><a href="#aws">AWS</a><ul>
|
||
<li><a href="#sagemaker">SageMaker</a></li>
|
||
</ul>
|
||
</li>
|
||
<li><a href="#azure">Azure</a><ul>
|
||
<li><a href="#mlflow">MLflow</a></li>
|
||
<li><a href="#model-serving">Model Serving</a></li>
|
||
<li><a href="#system-design">System Design</a></li>
|
||
</ul>
|
||
</li>
|
||
<li><a href="#flyte-managed">Flyte Managed</a></li>
|
||
<li><a href="#google-cloud">Google Cloud</a><ul>
|
||
<li><a href="#data-analytics">Data Analytics</a></li>
|
||
</ul>
|
||
</li>
|
||
<li><a href="#openshift">OpenShift</a><ul>
|
||
<li><a href="#enterprise-platforms">Enterprise Platforms</a></li>
|
||
</ul>
|
||
</li>
|
||
<li><a href="#data-engineering">Data Engineering</a></li>
|
||
<li><a href="#data-labeling">Data Labeling</a><ul>
|
||
<li><a href="#human-in-the-loop">Human-In-The-Loop</a></li>
|
||
</ul>
|
||
</li>
|
||
<li><a href="#data-ops">Data Ops</a><ul>
|
||
<li><a href="#ci-cd-1">CI-CD</a></li>
|
||
</ul>
|
||
</li>
|
||
<li><a href="#learning-roadmap">Learning Roadmap</a><ul>
|
||
<li><a href="#machine-learning">Machine Learning</a></li>
|
||
</ul>
|
||
</li>
|
||
<li><a href="#streaming">Streaming</a><ul>
|
||
<li><a href="#kafka">Kafka</a></li>
|
||
</ul>
|
||
</li>
|
||
<li><a href="#data-science">Data Science</a></li>
|
||
<li><a href="#career-guidance">Career Guidance</a><ul>
|
||
<li><a href="#industry-trends">Industry Trends</a></li>
|
||
</ul>
|
||
</li>
|
||
<li><a href="#cloud-notebooks">Cloud Notebooks</a><ul>
|
||
<li><a href="#data-engineering-1">Data Engineering</a></li>
|
||
</ul>
|
||
</li>
|
||
<li><a href="#data-frames">Data Frames</a><ul>
|
||
<li><a href="#out-of-core-processing">Out-of-Core Processing</a></li>
|
||
</ul>
|
||
</li>
|
||
<li><a href="#datasets">Datasets</a><ul>
|
||
<li><a href="#medical-imaging">Medical Imaging</a></li>
|
||
<li><a href="#regression-analysis">Regression Analysis</a></li>
|
||
</ul>
|
||
</li>
|
||
<li><a href="#developer-tooling">Developer Tooling</a><ul>
|
||
<li><a href="#python-ecosystem">Python Ecosystem</a></li>
|
||
</ul>
|
||
</li>
|
||
<li><a href="#data-version-control">Data Version Control</a></li>
|
||
<li><a href="#developer-tooling-1">Developer Tooling</a></li>
|
||
<li><a href="#deployment">Deployment</a></li>
|
||
<li><a href="#kubernetes-orchestration">Kubernetes Orchestration</a></li>
|
||
<li><a href="#development-environments">Development Environments</a></li>
|
||
<li><a href="#containerization">Containerization</a></li>
|
||
<li><a href="#distributed-systems">Distributed Systems</a></li>
|
||
<li><a href="#compute-engines">Compute Engines</a><ul>
|
||
<li><a href="#ray">Ray</a></li>
|
||
</ul>
|
||
</li>
|
||
<li><a href="#experiment-tracking">Experiment Tracking</a></li>
|
||
<li><a href="#visualization">Visualization</a></li>
|
||
<li><a href="#generative-ai">Generative AI</a></li>
|
||
<li><a href="#distributed-systems-1">Distributed Systems</a><ul>
|
||
<li><a href="#case-studies">Case Studies</a></li>
|
||
</ul>
|
||
</li>
|
||
<li><a href="#llm-ops">LLM Ops</a><ul>
|
||
<li><a href="#aws-1">AWS</a></li>
|
||
<li><a href="#self-assessment">Self-Assessment</a></li>
|
||
<li><a href="#system-design-1">System Design</a></li>
|
||
</ul>
|
||
</li>
|
||
<li><a href="#governance">Governance</a></li>
|
||
<li><a href="#maturity-models">Maturity Models</a></li>
|
||
<li><a href="#infrastructure">Infrastructure</a></li>
|
||
<li><a href="#container-runtimes">Container Runtimes</a><ul>
|
||
<li><a href="#gpu-drivers">GPU Drivers</a></li>
|
||
</ul>
|
||
</li>
|
||
<li><a href="#gpu-monitoring">GPU Monitoring</a><ul>
|
||
<li><a href="#command-line-tools">Command Line Tools</a></li>
|
||
</ul>
|
||
</li>
|
||
<li><a href="#gpu-orchestration">GPU Orchestration</a><ul>
|
||
<li><a href="#kubernetes-operators">Kubernetes Operators</a></li>
|
||
<li><a href="#kubernetes-setup">Kubernetes Setup</a></li>
|
||
<li><a href="#nix-package-manager">Nix Package Manager</a></li>
|
||
<li><a href="#resource-allocation">Resource Allocation</a></li>
|
||
</ul>
|
||
</li>
|
||
<li><a href="#kubernetes">Kubernetes</a></li>
|
||
<li><a href="#architectural-patterns">Architectural Patterns</a></li>
|
||
<li><a href="#artifact-registration">Artifact Registration</a></li>
|
||
<li><a href="#component-engineering">Component Engineering</a></li>
|
||
<li><a href="#deployment-guides">Deployment Guides</a></li>
|
||
<li><a href="#kubeflow">Kubeflow</a></li>
|
||
<li><a href="#learning-roadmap-1">Learning Roadmap</a></li>
|
||
<li><a href="#courses">Courses</a></li>
|
||
<li><a href="#curated-reference">Curated Reference</a></li>
|
||
<li><a href="#machine-learning-1">Machine Learning</a></li>
|
||
<li><a href="#competitions">Competitions</a><ul>
|
||
<li><a href="#datasets-1">Datasets</a></li>
|
||
</ul>
|
||
</li>
|
||
<li><a href="#computer-vision">Computer Vision</a><ul>
|
||
<li><a href="#instance-segmentation">Instance Segmentation</a></li>
|
||
</ul>
|
||
</li>
|
||
<li><a href="#databases">Databases</a><ul>
|
||
<li><a href="#in-database-ml">In-database ML</a></li>
|
||
</ul>
|
||
</li>
|
||
<li><a href="#distributed-training">Distributed Training</a><ul>
|
||
<li><a href="#fault-tolerance">Fault Tolerance</a></li>
|
||
</ul>
|
||
</li>
|
||
<li><a href="#document-analysis">Document Analysis</a><ul>
|
||
<li><a href="#ocr">OCR</a></li>
|
||
</ul>
|
||
</li>
|
||
<li><a href="#education">Education</a><ul>
|
||
<li><a href="#study-materials">Study Materials</a></li>
|
||
</ul>
|
||
</li>
|
||
<li><a href="#foundations-1">Foundations</a><ul>
|
||
<li><a href="#scratch-implementations">Scratch Implementations</a></li>
|
||
</ul>
|
||
</li>
|
||
<li><a href="#information-retrieval">Information Retrieval</a><ul>
|
||
<li><a href="#rag-pipelines">RAG Pipelines</a></li>
|
||
</ul>
|
||
</li>
|
||
<li><a href="#large-language-models">Large Language Models</a><ul>
|
||
<li><a href="#fine-tuning">Fine-tuning</a></li>
|
||
</ul>
|
||
</li>
|
||
<li><a href="#mlops">MLOps</a><ul>
|
||
<li><a href="#model-pipelines">Model Pipelines</a></li>
|
||
</ul>
|
||
</li>
|
||
<li><a href="#medical-imaging-1">Medical Imaging</a><ul>
|
||
<li><a href="#computer-vision-1">Computer Vision</a></li>
|
||
</ul>
|
||
</li>
|
||
<li><a href="#medical-imaging-2">Medical Imaging</a></li>
|
||
<li><a href="#end-to-end-pipeline">End-to-End Pipeline</a></li>
|
||
<li><a href="#model-life-cycle">Model Life Cycle</a></li>
|
||
<li><a href="#aws-2">AWS</a></li>
|
||
<li><a href="#enterprise-patterns">Enterprise Patterns</a></li>
|
||
<li><a href="#model-serving-1">Model Serving</a></li>
|
||
<li><a href="#api-development">API Development</a><ul>
|
||
<li><a href="#fastapi">FastAPI</a></li>
|
||
</ul>
|
||
</li>
|
||
<li><a href="#architectural-patterns-1">Architectural Patterns</a><ul>
|
||
<li><a href="#infrastructure-selection">Infrastructure Selection</a></li>
|
||
</ul>
|
||
</li>
|
||
<li><a href="#kubernetes-1">Kubernetes</a><ul>
|
||
<li><a href="#kserve">KServe</a></li>
|
||
</ul>
|
||
</li>
|
||
<li><a href="#microservices">Microservices</a></li>
|
||
<li><a href="#orchestration">Orchestration</a></li>
|
||
<li><a href="#airflow">Airflow</a></li>
|
||
<li><a href="#airflow-providers">Airflow Providers</a></li>
|
||
<li><a href="#comparative-analysis">Comparative Analysis</a></li>
|
||
<li><a href="#flyte">Flyte</a></li>
|
||
<li><a href="#frameworks">Frameworks</a></li>
|
||
<li><a href="#workflows">Workflows</a></li>
|
||
<li><a href="#software-engineering">Software Engineering</a></li>
|
||
<li><a href="#devops-1">DevOps</a><ul>
|
||
<li><a href="#ai-assisted-workflows">AI-Assisted Workflows</a></li>
|
||
</ul>
|
||
</li>
|
||
<li><a href="#workflow-transition">Workflow Transition</a></li>
|
||
<li><a href="#best-practices">Best Practices</a></li>
|
||
<li><a href="#workshops">Workshops</a></li>
|
||
<li><a href="#infrastructure-1">Infrastructure</a></li>
|
||
</ol>
|
||
<h2 id="ai-and-machine-learning">AI and Machine Learning<a class="headerlink" href="#ai-and-machine-learning" title="Permanent link">¶</a></h2>
|
||
<h3 id="bio-computing">Bio-Computing<a class="headerlink" href="#bio-computing" title="Permanent link">¶</a></h3>
|
||
<h4 id="protein-folding">Protein Folding<a class="headerlink" href="#protein-folding" title="Permanent link">¶</a></h4>
|
||
<ul>
|
||
<li><strong>(2022)</strong> <a href="https://www.elconfidencial.com/tecnologia/ciencia/2022-11-18/carrera-google-meta-revolucionar-biologia_3520865">elconfidencial.com: La batalla entre Google y Meta que nadie esperaba: revolucionar la biología 🌟</a> <span class='md-tag md-tag--warning'>[SPANISH CONTENT]</span> 🌟🌟 <span class='md-tag md-tag--warning'>[EMERGING]</span> — A deep journalistic analysis exploring the intense scientific competition between Google's DeepMind (AlphaFold) and Meta AI (ESMFold) in protein folding. Highlights the tectonic shift of biological science into a computationally intensive machine learning domain with staggering computing architecture demands.</li>
|
||
</ul>
|
||
<h3 id="foundations">Foundations<a class="headerlink" href="#foundations" title="Permanent link">¶</a></h3>
|
||
<h4 id="decision-intelligence">Decision Intelligence<a class="headerlink" href="#decision-intelligence" title="Permanent link">¶</a></h4>
|
||
<ul>
|
||
<li><strong>(2020)</strong> <a href="https://www.youtube.com/playlist?list=PLRKtJ4IpxJpDxl0NTvNYQWKCYzHNuy2xG"><strong>youtube: Making Friends with Machine Learning | Cassie Kozyrkov | playlist 🌟</strong></a> <span class='md-tag md-tag--warning'>[NONE CONTENT]</span> 🌟🌟🌟🌟 <span class='md-tag md-tag--success'>[ENTERPRISE-STABLE]</span> — A foundational lecture series on structural decision-making and pragmatism in machine learning by Cassie Kozyrkov. Focuses on framing analytics problems, mitigating cognitive biases in model creation, and designing human-centric metrics to measure real business impact.</li>
|
||
</ul>
|
||
<h4 id="neural-networks">Neural Networks<a class="headerlink" href="#neural-networks" title="Permanent link">¶</a></h4>
|
||
<ul>
|
||
<li><strong>(2023)</strong> <a href="https://www.freecodecamp.org/news/learn-machine-learning-and-neural-networks-without-frameworks">marvelousmlops.substack.com: Learn Machine Learning and Neural Networks without Frameworks</a> <span class='md-tag md-tag--warning'>[PYTHON CONTENT]</span> 🌟🌟🌟 <span class='md-tag md-tag--info'>[COMMUNITY-TOOL]</span> — A technical exploration of the math and implementation mechanics of neural networks from pure scratch, avoiding PyTorch or TensorFlow abstraction layers. Focuses on matrix operations, backpropagation algorithms, and loss optimizations to build strong foundational knowledge.</li>
|
||
</ul>
|
||
<h3 id="open-source">Open Source<a class="headerlink" href="#open-source" title="Permanent link">¶</a></h3>
|
||
<h4 id="landscape-evaluation">Landscape Evaluation<a class="headerlink" href="#landscape-evaluation" title="Permanent link">¶</a></h4>
|
||
<ul>
|
||
<li><strong>(2022)</strong> <a href="https://www.infoworld.com/article/2336757/16-open-source-projects-transforming-ai-and-machine-learning.html">infoworld.com: 13 open source projects transforming AI and machine learning</a> <span class='md-tag md-tag--warning'>[NONE CONTENT]</span> 🌟🌟🌟 <span class='md-tag md-tag--warning'>[EMERGING]</span> — An analytical review of thirteen disruptive open-source initiatives restructuring the artificial intelligence and machine learning paradigm. Covers emerging runtimes, foundational data lakes, model-serving layers, and high-performance training acceleration engines that dominate the landscape.</li>
|
||
</ul>
|
||
<h2 id="architectural-foundations">Architectural Foundations<a class="headerlink" href="#architectural-foundations" title="Permanent link">¶</a></h2>
|
||
<h3 id="kubernetes-tools">Kubernetes Tools<a class="headerlink" href="#kubernetes-tools" title="Permanent link">¶</a></h3>
|
||
<h4 id="general-reference">General Reference<a class="headerlink" href="#general-reference" title="Permanent link">¶</a></h4>
|
||
<ul>
|
||
<li><a href="https://cd.foundation/blog/2020/02/11/announcing-the-cd-foundation-mlops-sig">cd.foundation: Announcing the CD Foundation MLOps SIG</a> <span class='md-tag md-tag--info'>[COMMUNITY-TOOL]</span> — A curated technical resource and architectural guide covering cd.foundation: Announcing the CD Foundation MLOps SIG in the Kubernetes Tools ecosystem.</li>
|
||
<li><a href="https://www.ravirajag.dev/blog/mlops-summary">ravirajag.dev: MLOps Basics - Week 10: Summary</a> <span class='md-tag md-tag--info'>[COMMUNITY-TOOL]</span> — A curated technical resource and architectural guide covering ravirajag.dev: MLOps Basics - Week 10: Summary in the Kubernetes Tools ecosystem.</li>
|
||
<li><a href="https://medium.com/workday-engineering/implementing-a-fully-automated-sharding-strategy-on-kubernetes-for-multi-tenanted-machine-learning-4371c48122ae">medium.com/workday-engineering: Implementing a Fully Automated Sharding' Strategy on Kubernetes for Multi-tenanted Machine Learning Applications</a> <span class='md-tag md-tag--info'>[COMMUNITY-TOOL]</span> — A curated technical resource and architectural guide covering medium.com/workday-engineering: Implementing a Fully Automated Sharding' Strategy on Kubernetes for Multi-tenanted Machine Learning Applications in the Kubernetes Tools ecosystem.</li>
|
||
<li><a href="https://medium.com/globant/advantages-of-deploying-machine-learning-models-with-kubernetes-8454cc7c565e">medium.com/globant: Advantages of Deploying Machine Learning models with' Kubernetes 🌟</a> <span class='md-tag md-tag--info'>[COMMUNITY-TOOL]</span> — A curated technical resource and architectural guide covering <mark>medium.com/globant: Advantages of Deploying Machine Learning models with' Kubernetes</mark> 🌟 in the Kubernetes Tools ecosystem.</li>
|
||
<li><a href="https://medium.com/pythoneers/mlops-tool-stack-requirement-in-machine-learning-pipeline-474b39f09dfc">medium.com/pythoneers: MLOps: Tool Stack Requirement in Machine Learning' Pipeline</a> <span class='md-tag md-tag--info'>[COMMUNITY-TOOL]</span> — A curated technical resource and architectural guide covering medium.com/pythoneers: MLOps: Tool Stack Requirement in Machine Learning' Pipeline in the Kubernetes Tools ecosystem.</li>
|
||
<li><a href="https://medium.com/formaloo/making-databases-as-easy-as-playing-with-legos-no-code-no-problem-ed41d4fde269">medium.com/formaloo: How no-code platforms are democratizing data science' and software development 🌟</a> <span class='md-tag md-tag--info'>[COMMUNITY-TOOL]</span> — A curated technical resource and architectural guide covering medium.com/formaloo: How no-code platforms are democratizing data science' and software development 🌟 in the Kubernetes Tools ecosystem.</li>
|
||
<li><a href="https://www.datarevenue.com/en-blog/airflow-vs-luigi-vs-argo-vs-mlflow-vs-kubeflow">datarevenue.com: Airflow vs. Luigi vs. Argo vs. MLFlow vs. KubeFlow</a> <span class='md-tag md-tag--info'>[COMMUNITY-TOOL]</span> — A curated technical resource and architectural guide covering datarevenue.com: Airflow vs. Luigi vs. Argo vs. MLFlow vs. KubeFlow in the Kubernetes Tools ecosystem.</li>
|
||
<li><a href="https://swirlai.substack.com/p/sai-08-request-response-model-deployment">swirlai.substack.com: SAI #08: Request-Response Model Deployment - The MLOps' Way, Spark - Executor Memory Structure and more... 🌟</a> <span class='md-tag md-tag--info'>[COMMUNITY-TOOL]</span> — A curated technical resource and architectural guide covering swirlai.substack.com: SAI #08: Request-Response Model Deployment - The MLOps' Way, Spark - Executor Memory Structure and more... 🌟 in the Kubernetes Tools ecosystem.</li>
|
||
<li><a href="https://openai.com/research/scaling-kubernetes-to-7500-nodes">openai.com: Scaling Kubernetes to 7,500 nodes 🌟</a> <span class='md-tag md-tag--info'>[COMMUNITY-TOOL]</span> — A curated technical resource and architectural guide covering openai.com: Scaling Kubernetes to 7,500 nodes 🌟 in the Kubernetes Tools ecosystem.</li>
|
||
<li><a href="https://medium.com/@study.uttam/main-challenges-of-machine-learning-eb06dffac3da">medium.com/@study.uttam: Main Challenges of Machine Learning</a> <span class='md-tag md-tag--info'>[COMMUNITY-TOOL]</span> — A curated technical resource and architectural guide covering medium.com/@study.uttam: Main Challenges of Machine Learning in the Kubernetes Tools ecosystem.</li>
|
||
<li><a href="https://medium.com/ai-hero/streamlining-machine-learning-operations-with-kubernetes-and-terraform-41baad37998e">medium.com/ai-hero: Streamlining Machine Learning Operations (MLOps) with' Kubernetes and Terraform</a> <span class='md-tag md-tag--info'>[COMMUNITY-TOOL]</span> — A curated technical resource and architectural guide covering medium.com/ai-hero: Streamlining Machine Learning Operations (MLOps) with' Kubernetes and Terraform in the Kubernetes Tools ecosystem.</li>
|
||
<li><a href="https://medium.com/@karanshingde/machine-learning-in-production-your-comprehensive-101-practical-guide-c7de0b5ad011">medium.com/@karanshingde: Machine Learning in Production— Your Comprehensive' 101 Practical Guide</a> <span class='md-tag md-tag--info'>[COMMUNITY-TOOL]</span> — A curated technical resource and architectural guide covering medium.com/@karanshingde: Machine Learning in Production— Your Comprehensive' 101 Practical Guide in the Kubernetes Tools ecosystem.</li>
|
||
<li><a href="https://marvelousmlops.substack.com/p/cicd-for-mlops-on-gitlab-part-1">marvelousmlops.substack.com: CI/CD for MLOps on GitLab (part 1)</a> <span class='md-tag md-tag--info'>[COMMUNITY-TOOL]</span> — A curated technical resource and architectural guide covering marvelousmlops.substack.com: CI/CD for MLOps on GitLab (part 1) in the Kubernetes Tools ecosystem.</li>
|
||
<li><a href="https://medium.com/aiguys/mlops-serving-ai-to-million-users-c77ed718b7ed">medium.com/aiguys: MLOps: Serving AI apps to million users</a> <span class='md-tag md-tag--info'>[COMMUNITY-TOOL]</span> — A curated technical resource and architectural guide covering medium.com/aiguys: MLOps: Serving AI apps to million users in the Kubernetes Tools ecosystem.</li>
|
||
<li><a href="https://marvelousmlops.substack.com/p/how-to-sell-mlops-in-large-organizations">marvelousmlops.substack.com: How to sell MLOps in large Organizations</a> <span class='md-tag md-tag--info'>[COMMUNITY-TOOL]</span> — A curated technical resource and architectural guide covering marvelousmlops.substack.com: How to sell MLOps in large Organizations in the Kubernetes Tools ecosystem.</li>
|
||
<li><a href="https://marvelousmlops.substack.com/p/mlops-roadmap-2024">marvelousmlops.substack.com: MLOps roadmap 2024</a> <span class='md-tag md-tag--info'>[COMMUNITY-TOOL]</span> — A curated technical resource and architectural guide covering marvelousmlops.substack.com: MLOps roadmap 2024 in the Kubernetes Tools ecosystem.</li>
|
||
<li><a href="https://marvelousmlops.substack.com/p/technical-roles-in-data-science-who">marvelousmlops.substack.com: Technical roles in Data Science: Who is doing' what?</a> <span class='md-tag md-tag--info'>[COMMUNITY-TOOL]</span> — A curated technical resource and architectural guide covering marvelousmlops.substack.com: Technical roles in Data Science: Who is doing' what? in the Kubernetes Tools ecosystem.</li>
|
||
<li><a href="https://marvelousmlops.substack.com/p/traceability-and-reproducibility">marvelousmlops.substack.com: Traceability & Reproducibility</a> <span class='md-tag md-tag--info'>[COMMUNITY-TOOL]</span> — A curated technical resource and architectural guide covering marvelousmlops.substack.com: Traceability & Reproducibility in the Kubernetes Tools ecosystem.</li>
|
||
<li><a href="https://seattledataguy.substack.com/p/data-engineering-vs-machine-learning">seattledataguy.substack.com: Data Engineering Vs Machine Learning Pipelines</a> <span class='md-tag md-tag--info'>[COMMUNITY-TOOL]</span> — A curated technical resource and architectural guide covering <mark>seattledataguy.substack.com: Data Engineering Vs Machine Learning Pipelines</mark> in the Kubernetes Tools ecosystem.</li>
|
||
<li><a href="https://medium.com/@samiullah6799/different-roles-in-mlops-0918de5321a4">medium.com/@samiullah6799: Different Roles in MLOps</a> <span class='md-tag md-tag--info'>[COMMUNITY-TOOL]</span> — A curated technical resource and architectural guide covering <mark>medium.com/@samiullah6799: Different Roles in MLOps</mark> in the Kubernetes Tools ecosystem.</li>
|
||
<li><a href="https://marvelousmlops.substack.com/p/sharpen-your-cookiecutter-speed-up">marvelousmlops.substack.com: Sharpen your cookiecutter: speed up repo creation' with workflows</a> <span class='md-tag md-tag--info'>[COMMUNITY-TOOL]</span> — A curated technical resource and architectural guide covering marvelousmlops.substack.com: Sharpen your cookiecutter: speed up repo creation' with workflows in the Kubernetes Tools ecosystem.</li>
|
||
<li><a href="https://decodingml.substack.com/p/how-to-ensure-your-models-are-fail">decodingml.substack.com: How to ensure your models are fail-safe in production?</a> <span class='md-tag md-tag--info'>[COMMUNITY-TOOL]</span> — A curated technical resource and architectural guide covering decodingml.substack.com: How to ensure your models are fail-safe in production? in the Kubernetes Tools ecosystem.</li>
|
||
<li><a href="https://medium.com/@kevin30101999/machine-learning-pipeline-using-argo-workflow-be91feb07c41">medium.com/@kevin30101999: Machine Learning Pipeline using Argo workflow' 🌟</a> <span class='md-tag md-tag--info'>[COMMUNITY-TOOL]</span> — A curated technical resource and architectural guide covering medium.com/@kevin30101999: Machine Learning Pipeline using Argo workflow' 🌟 in the Kubernetes Tools ecosystem.</li>
|
||
<li><a href="https://marvelousmlops.substack.com">Marvelous MLOps Substack</a> <span class='md-tag md-tag--info'>[COMMUNITY-TOOL]</span> — A curated technical resource and architectural guide covering Marvelous MLOps Substack in the Kubernetes Tools ecosystem.</li>
|
||
<li><a href="https://decodingml.substack.com">decodingml.substack.com: Decoding ML Newsletter</a> <span class='md-tag md-tag--info'>[COMMUNITY-TOOL]</span> — A curated technical resource and architectural guide covering decodingml.substack.com: Decoding ML Newsletter in the Kubernetes Tools ecosystem.</li>
|
||
<li><a href="https://medium.com/mlearning-ai/the-best-object-detection-libraries-that-i-work-with-835428a1e01e">medium.com/mlearning-ai: The Best Object Detection Libraries That I Work' With</a> <span class='md-tag md-tag--info'>[COMMUNITY-TOOL]</span> — A curated technical resource and architectural guide covering medium.com/mlearning-ai: The Best Object Detection Libraries That I Work' With in the Kubernetes Tools ecosystem.</li>
|
||
<li><a href="https://blog.devgenius.io/kubeflow-cloud-deployment-aws-46f739ccbb32">blog.devgenius.io: Kubeflow Cloud Deployment (AWS)</a> <span class='md-tag md-tag--info'>[COMMUNITY-TOOL]</span> — A curated technical resource and architectural guide covering blog.devgenius.io: Kubeflow Cloud Deployment (AWS) in the Kubernetes Tools ecosystem.</li>
|
||
<li><a href="https://joseprsm.medium.com/how-to-build-machine-learning-models-that-train-themselves-bbc87499ca5">joseprsm.medium.com: How to build Machine Learning models that train themselves</a> <span class='md-tag md-tag--info'>[COMMUNITY-TOOL]</span> — A curated technical resource and architectural guide covering joseprsm.medium.com: How to build Machine Learning models that train themselves in the Kubernetes Tools ecosystem.</li>
|
||
<li><a href="https://medium.com/dkatalis/creating-a-mutating-webhook-for-great-good-b21acb941207">medium.com/dkatalis: Creating a Mutating Webhook for Great Good! Or: how' to automatically provision Pods on a specific node pool</a> <span class='md-tag md-tag--info'>[COMMUNITY-TOOL]</span> — A curated technical resource and architectural guide covering medium.com/dkatalis: Creating a Mutating Webhook for Great Good! Or: how' to automatically provision Pods on a specific node pool in the Kubernetes Tools ecosystem.</li>
|
||
<li><a href="https://stackoverflow.com/questions/72657318/how-is-flyte-tailored-to-data-and-machine-learning">stackoverflow.com: How is Flyte tailored to "Data and Machine Learning"?</a> <span class='md-tag md-tag--info'>[COMMUNITY-TOOL]</span> — A curated technical resource and architectural guide covering stackoverflow.com: How is Flyte tailored to "Data and Machine Learning"? in the Kubernetes Tools ecosystem.</li>
|
||
<li><a href="https://medium.com/@timleonardDS/who-lets-the-dags-out-register-an-external-dag-with-flyte-chapter-3-bad0ea781119">medium.com/@timleonardDS: Who Let the DAGs out? Register an External DAG' with Flyte (Chapter 3)</a> <span class='md-tag md-tag--info'>[COMMUNITY-TOOL]</span> — A curated technical resource and architectural guide covering medium.com/@timleonardDS: Who Let the DAGs out? Register an External DAG' with Flyte (Chapter 3) in the Kubernetes Tools ecosystem.</li>
|
||
<li><a href="https://blog.devops.dev/mastering-machine-learning-at-scale-with-azure-machine-learning-dfaa4bf4353c">blog.devops.dev: Mastering Machine Learning at Scale with Azure Machine' Learning</a> <span class='md-tag md-tag--info'>[COMMUNITY-TOOL]</span> — A curated technical resource and architectural guide covering blog.devops.dev: Mastering Machine Learning at Scale with Azure Machine' Learning in the Kubernetes Tools ecosystem.</li>
|
||
<li><a href="https://marvelousmlops.substack.com/p/model-serving-architectures-on-databricks">marvelousmlops.substack.com: Model serving architectures on Databricks</a> <span class='md-tag md-tag--info'>[COMMUNITY-TOOL]</span> — A curated technical resource and architectural guide covering marvelousmlops.substack.com: Model serving architectures on Databricks in the Kubernetes Tools ecosystem.</li>
|
||
<li><a href="https://medium.com/sync-computing/top-9-lessons-learned-about-databricks-jobs-serverless-41a43e99ded5">medium.com/sync-computing: Top 9 Lessons Learned about Databricks Jobs Serverless</a> <span class='md-tag md-tag--info'>[COMMUNITY-TOOL]</span> — A curated technical resource and architectural guide covering medium.com/sync-computing: Top 9 Lessons Learned about Databricks Jobs Serverless in the Kubernetes Tools ecosystem.</li>
|
||
<li><a href="https://medium.com/bakdata/scalable-machine-learning-with-kafka-streams-and-kserve-85308858d867">medium.com/bakdata: Scalable Machine Learning with Kafka Streams and KServe</a> <span class='md-tag md-tag--info'>[COMMUNITY-TOOL]</span> — A curated technical resource and architectural guide covering medium.com/bakdata: Scalable Machine Learning with Kafka Streams and KServe in the Kubernetes Tools ecosystem.</li>
|
||
<li><a href="https://medium.com/@bchenjh/full-fine-tuning-of-llama2-on-kubernetes-a983e1eb2259">medium.com/@bchenjh: Distributed full fine-tuning of Llama2 on Kubernetes</a> <span class='md-tag md-tag--info'>[COMMUNITY-TOOL]</span> — A curated technical resource and architectural guide covering medium.com/@bchenjh: Distributed full fine-tuning of Llama2 on Kubernetes in the Kubernetes Tools ecosystem.</li>
|
||
<li><a href="https://learn.iterative.ai">learn.iterative.ai: Iterative Tools for Data Scientists & Analysts</a> <span class='md-tag md-tag--info'>[COMMUNITY-TOOL]</span> — A curated technical resource and architectural guide covering learn.iterative.ai: Iterative Tools for Data Scientists & Analysts in the Kubernetes Tools ecosystem.</li>
|
||
<li><a href="https://blog.devgenius.io/training-model-with-jenkins-using-docker-mlops-b18579ddb677">blog.devgenius.io: Training model with Jenkins using docker: MLOPS</a> <span class='md-tag md-tag--info'>[COMMUNITY-TOOL]</span> — A curated technical resource and architectural guide covering blog.devgenius.io: Training model with Jenkins using docker: MLOPS in the Kubernetes Tools ecosystem.</li>
|
||
<li><a href="https://betterprogramming.pub/attach-a-visual-debugger-to-ml-training-jobs-on-kubernetes-eb9678389f1f">betterprogramming.pub: Attach a Visual Debugger to ML-training Jobs on Kubernetes</a> <span class='md-tag md-tag--info'>[COMMUNITY-TOOL]</span> — A curated technical resource and architectural guide covering betterprogramming.pub: Attach a Visual Debugger to ML-training Jobs on Kubernetes in the Kubernetes Tools ecosystem.</li>
|
||
</ul>
|
||
<h2 id="automl">AutoML<a class="headerlink" href="#automl" title="Permanent link">¶</a></h2>
|
||
<h3 id="low-code">Low-Code<a class="headerlink" href="#low-code" title="Permanent link">¶</a></h3>
|
||
<h4 id="code-generation">Code Generation<a class="headerlink" href="#code-generation" title="Permanent link">¶</a></h4>
|
||
<ul>
|
||
<li><strong>(2021)</strong> <a href="https://towardsdatascience.com/automatically-generate-machine-learning-code-with-just-a-few-clicks-7901b2334f97">towardsdatascience.com: Automatically Generate Machine Learning Code with Just a Few Clicks</a> <span class='md-tag md-tag--warning'>[PYTHON CONTENT]</span> 🌟🌟 <span class='md-tag md-tag--info'>[COMMUNITY-TOOL]</span> — Explores early AutoML and automated code-generation tools designed to accelerate the model-building lifecycle. Evaluates the architectural benefits of removing manual scaffolding from pipeline creation, while stressing the long-term necessity of custom code refactoring for performance-critical production systems.</li>
|
||
</ul>
|
||
<h2 id="ci-cd">CI-CD<a class="headerlink" href="#ci-cd" title="Permanent link">¶</a></h2>
|
||
<h3 id="devops">DevOps<a class="headerlink" href="#devops" title="Permanent link">¶</a></h3>
|
||
<ul>
|
||
<li><strong>(2021)</strong> <a href="https://analyticsindiamag.com/top-tools-for-enabling-ci-cd-in-ml-pipelines">analyticsindiamag.com: Top tools for enabling CI/CD in ML pipelines</a> <span class='md-tag md-tag--warning'>[NONE CONTENT]</span> 🌟🌟🌟 <span class='md-tag md-tag--info'>[COMMUNITY-TOOL]</span> — Evaluates prominent orchestration and CI/CD tools targeted at ML pipelines, comparing systems like Jenkins, GitHub Actions, and specialized MLOps runners. Discusses the fundamental differences between traditional software compilation and ML pipelines that require data versioning and model validation.</li>
|
||
</ul>
|
||
<h2 id="cicd">CICD<a class="headerlink" href="#cicd" title="Permanent link">¶</a></h2>
|
||
<h3 id="containers">Containers<a class="headerlink" href="#containers" title="Permanent link">¶</a></h3>
|
||
<ul>
|
||
<li><strong>(2021)</strong> <a href="https://towardsdatascience.com/from-devops-to-mlops-integrate-machine-learning-models-using-jenkins-and-docker-79034dbedf1">towardsdatascience.com: From DevOps to MLOPS: Integrate Machine Learning Models using Jenkins and Docker</a> <span class='md-tag md-tag--warning'>[PYTHON CONTENT]</span> <span class='md-tag md-tag--info'>[COMMUNITY-TOOL]</span> — This technical guide bridges DevOps and MLOps by demonstrating how to containerize machine learning models using Docker and orchestrate their integration pipelines with Jenkins. It provides an architectural map for automating CI/CD routines for smart microservices, ensuring reproducible builds and robust test suites.</li>
|
||
</ul>
|
||
<h2 id="cloud-platforms">Cloud Platforms<a class="headerlink" href="#cloud-platforms" title="Permanent link">¶</a></h2>
|
||
<h3 id="aws">AWS<a class="headerlink" href="#aws" title="Permanent link">¶</a></h3>
|
||
<h4 id="sagemaker">SageMaker<a class="headerlink" href="#sagemaker" title="Permanent link">¶</a></h4>
|
||
<ul>
|
||
<li><strong>(2022)</strong> <a href="https://aws.amazon.com/blogs/machine-learning/mlops-foundation-roadmap-for-enterprises-with-amazon-sagemaker"><strong>aws.amazon.com: MLOps foundation roadmap for enterprises with Amazon SageMaker</strong></a> <span class='md-tag md-tag--warning'>[NONE CONTENT]</span> <span class='md-tag md-tag--critical'>[ADVANCED LEVEL]</span> 🌟🌟🌟🌟 <span class='md-tag md-tag--success'>[ENTERPRISE-STABLE]</span> — A detailed blueprints roadmap by AWS outlining standard enterprise implementation strategies for SageMaker. Addresses resource provisioning, continuous training cycles, multi-account governance, and secure networking architectures required for heavy-duty financial and healthcare deployments.</li>
|
||
<li><strong>(2022)</strong> <a href="https://aws.amazon.com/blogs/machine-learning/promote-pipelines-in-a-multi-environment-setup-using-amazon-sagemaker-model-registry-hashicorp-terraform-github-and-jenkins-ci-cd"><strong>aws.amazon.com: Promote pipelines in a multi-environment setup using Amazon SageMaker Model Registry, HashiCorp Terraform, GitHub, and Jenkins CI/CD</strong></a> <span class='md-tag md-tag--warning'>[HCL CONTENT]</span> <span class='md-tag md-tag--critical'>[ADVANCED LEVEL]</span> 🌟🌟🌟🌟 <span class='md-tag md-tag--success'>[ENTERPRISE-STABLE]</span> — An advanced infrastructure-as-code deployment blueprint for multi-environment (Dev, Stage, Prod) MLOps promotion. Orchestrates SageMaker Model Registry with HashiCorp Terraform, GitHub Actions, and Jenkins, demonstrating an enterprise-grade, highly-secured CI/CD model delivery loop.</li>
|
||
</ul>
|
||
<h3 id="azure">Azure<a class="headerlink" href="#azure" title="Permanent link">¶</a></h3>
|
||
<h4 id="mlflow">MLflow<a class="headerlink" href="#mlflow" title="Permanent link">¶</a></h4>
|
||
<ul>
|
||
<li><strong>(2022)</strong> <a href="https://learn.microsoft.com/en-us/azure/machine-learning/concept-mlflow?view=azureml-api-2"><strong>docs.microsoft.com: MLflow and Azure Machine Learning</strong></a> <span class='md-tag md-tag--warning'>[PYTHON CONTENT]</span> <span class='md-tag md-tag--primary'>[DOCUMENTATION]</span> 🌟🌟🌟🌟 <span class='md-tag md-tag--success'>[ENTERPRISE-STABLE]</span> <span class='md-tag md-tag--critical'>[LEGACY]</span> — Detailed technical guide illustrating the native integration between MLflow APIs and Azure Machine Learning workspaces. Explains how developers can track local experiments directly to Azure ML cloud runs and publish models to Azure managed registries without rewriting legacy MLflow scripts.</li>
|
||
</ul>
|
||
<h4 id="model-serving">Model Serving<a class="headerlink" href="#model-serving" title="Permanent link">¶</a></h4>
|
||
<ul>
|
||
<li><strong>(2022)</strong> <a href="https://bea.stollnitz.com/blog/aml-batch-endpoint">bea.stollnitz.com: Creating batch endpoints in Azure ML</a> <span class='md-tag md-tag--warning'>[PYTHON CONTENT]</span> 🌟🌟🌟 <span class='md-tag md-tag--info'>[COMMUNITY-TOOL]</span> — Demystifies Azure ML batch endpoint configurations, highlighting the differences between batch and low-latency real-time managed endpoints. Covers execution environments, partition configurations, storage connections, and scaling parameters needed to serve heavy computational batch datasets efficiently.</li>
|
||
<li><strong>(2021)</strong> <a href="https://www.youtube.com/watch?v=6sqGxVI3X1w">youtube: Deploy Convolutional Neural Network (CNN) on Azure with Python | Deep Learning Deployment | MLOPS</a> <span class='md-tag md-tag--warning'>[PYTHON CONTENT]</span> 🌟🌟🌟 <span class='md-tag md-tag--info'>[COMMUNITY-TOOL]</span> — A practical video tutorial showing step-by-step preparation, containerization, and hosting of a Convolutional Neural Network (CNN) on Azure container endpoints. Demonstrates writing score scripts, declaring environment dependencies, and triggering predictions via REST APIs.</li>
|
||
</ul>
|
||
<h4 id="system-design">System Design<a class="headerlink" href="#system-design" title="Permanent link">¶</a></h4>
|
||
<ul>
|
||
<li><strong>(2023)</strong> <a href="https://learn.microsoft.com/en-us/azure/well-architected/service-guides/azure-machine-learning"><strong>learn.microsoft.com: Azure Well-Architected Framework perspective on Azure Machine Learning</strong></a> <span class='md-tag md-tag--warning'>[NONE CONTENT]</span> <span class='md-tag md-tag--critical'>[ADVANCED LEVEL]</span> <span class='md-tag md-tag--primary'>[DOCUMENTATION]</span> 🌟🌟🌟🌟 <span class='md-tag md-tag--success'>[ENTERPRISE-STABLE]</span> — Detailed architecture guidance map aligned to Azure's Well-Architected Framework, focused on Azure Machine Learning. Evaluates cost optimization, operational excellence, reliability, security architectures, and resource management models crucial for enterprise system compliance.</li>
|
||
</ul>
|
||
<h3 id="flyte-managed">Flyte Managed<a class="headerlink" href="#flyte-managed" title="Permanent link">¶</a></h3>
|
||
<ul>
|
||
<li><strong>(2024)</strong> <a href="https://www.union.ai"><strong>Union Cloud</strong></a> <span class='md-tag md-tag--warning'>[NONE CONTENT]</span> <span class='md-tag md-tag--critical'>[ADVANCED LEVEL]</span> 🌟🌟🌟🌟 <span class='md-tag md-tag--success'>[ENTERPRISE-STABLE]</span> — A managed enterprise platform powered by Flyte, designed to orchestrate complex machine learning and data engineering workloads. It delivers serverless operational abstraction, dynamic scaling, robust isolation structures, and unified lineage tracing across multi-cloud environments.</li>
|
||
</ul>
|
||
<h3 id="google-cloud">Google Cloud<a class="headerlink" href="#google-cloud" title="Permanent link">¶</a></h3>
|
||
<h4 id="data-analytics">Data Analytics<a class="headerlink" href="#data-analytics" title="Permanent link">¶</a></h4>
|
||
<ul>
|
||
<li><strong>(2021)</strong> <a href="https://cloud.google.com/blog/topics/developers-practitioners/how-use-machine-learning-model-google-sheet-using-bigquery-ml">cloud.google.com: How to use a machine learning model from a Google Sheet using BigQuery ML</a> <span class='md-tag md-tag--warning'>[SQL CONTENT]</span> 🌟🌟🌟 <span class='md-tag md-tag--info'>[COMMUNITY-TOOL]</span> — Explains how to integrate BigQuery ML model inferences directly within Google Sheets using connected sheets and SQL. This bridging pattern democratizes access to complex analytical models for non-technical stakeholders. Eliminates standard ETL overhead by pushing computation directly into Google's scalable data warehouse infrastructure.</li>
|
||
</ul>
|
||
<h3 id="openshift">OpenShift<a class="headerlink" href="#openshift" title="Permanent link">¶</a></h3>
|
||
<h4 id="enterprise-platforms">Enterprise Platforms<a class="headerlink" href="#enterprise-platforms" title="Permanent link">¶</a></h4>
|
||
<ul>
|
||
<li><strong>(2021)</strong> <a href="https://www.redhat.com/en/blog/introducing-red-hat-openshift-data-science"><strong>redhat.com: Introducing Red Hat OpenShift Data Science</strong></a> <span class='md-tag md-tag--warning'>[NONE CONTENT]</span> <span class='md-tag md-tag--critical'>[ADVANCED LEVEL]</span> 🌟🌟🌟🌟 <span class='md-tag md-tag--success'>[ENTERPRISE-STABLE]</span> — Technical introduction of Red Hat OpenShift Data Science (RHODS), a managed hybrid-cloud service leveraging OpenShift Kubernetes. Details out-of-the-box configurations for JupyterHub, PyTorch, TensorFlow, and partner tools like Starburst or Anaconda to simplify operational enterprise scaling.</li>
|
||
</ul>
|
||
<h2 id="data-engineering">Data Engineering<a class="headerlink" href="#data-engineering" title="Permanent link">¶</a></h2>
|
||
<h3 id="data-labeling">Data Labeling<a class="headerlink" href="#data-labeling" title="Permanent link">¶</a></h3>
|
||
<h4 id="human-in-the-loop">Human-In-The-Loop<a class="headerlink" href="#human-in-the-loop" title="Permanent link">¶</a></h4>
|
||
<ul>
|
||
<li><strong>(2023)</strong> <a href="https://github.com/argilla-io/argilla"><mark>rubrix</mark></a> <span class='md-tag md-tag--info'>⭐ 5002</span> <svg class="v2-sparkline" width="50" height="15" viewBox="0 0 50 15" style="vertical-align: middle; display: inline-block; margin-left: 6px;" title="Activity Trend"><defs><linearGradient id="spark-grad-cf4730fa" x1="0" y1="0" x2="1" y2="0"><stop offset="0%" stop-color="rgba(34, 211, 238, 0.2)" /><stop offset="100%" stop-color="var(--md-accent-fg-color)" /></linearGradient></defs><path class="v2-sparkline-path" d="M 0 3 L 10 13 L 20 10 L 30 7 L 40 8 L 50 5" fill="none" stroke="url(#spark-grad-cf4730fa)" stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round" /><circle cx="50" cy="5" r="2" fill="var(--md-accent-fg-color)" /></svg> <span class='md-tag md-tag--warning'>[PYTHON CONTENT]</span> 🌟🌟🌟🌟🌟 <span class='md-tag md-tag--success'>[DE FACTO STANDARD]</span> — Formerly Rubrix, Argilla is a premier open-source data curation platform designed for AI and LLM workflows. Enables continuous human-in-the-loop (HITL) fine-tuning cycles. It seamlessly integrates with Hugging Face, SpaCy, and LangChain, optimizing training data quality through iterative annotation, validation, and curation mechanisms.</li>
|
||
</ul>
|
||
<h3 id="data-ops">Data Ops<a class="headerlink" href="#data-ops" title="Permanent link">¶</a></h3>
|
||
<h4 id="ci-cd-1">CI-CD (1)<a class="headerlink" href="#ci-cd-1" title="Permanent link">¶</a></h4>
|
||
<ul>
|
||
<li><strong>(2022)</strong> <a href="https://semaphore.io/blog/devops-ml-data">semaphoreci.com: Why Do We Need DevOps for ML Data?</a> <span class='md-tag md-tag--warning'>[NONE CONTENT]</span> 🌟🌟🌟 <span class='md-tag md-tag--info'>[COMMUNITY-TOOL]</span> — Argues for the rigorous application of DevOps fundamentals to machine learning training data. Discusses concepts such as immutable data pipelines, data-drift unit testing, semantic versioning of large binary stores, and continuous integration validation applied specifically to high-volume datasets.</li>
|
||
</ul>
|
||
<h3 id="learning-roadmap">Learning Roadmap<a class="headerlink" href="#learning-roadmap" title="Permanent link">¶</a></h3>
|
||
<h4 id="machine-learning">Machine Learning<a class="headerlink" href="#machine-learning" title="Permanent link">¶</a></h4>
|
||
<ul>
|
||
<li><strong>(2023)</strong> <a href="https://github.com/abhishek-ch/around-dataengineering">github: A very Long never ending Learning around Data Engineering & Machine' Learning</a> <span class='md-tag md-tag--warning'>[PYTHON CONTENT]</span> 🌟🌟 <span class='md-tag md-tag--info'>[COMMUNITY-TOOL]</span> — A comprehensive curated repository documenting data engineering pipelines, distributed computing principles, and machine learning foundations. It aggregates core concepts of large-scale data systems, covering ingest-to-model-delivery workflows. Essential for developers transitioning from traditional software engineering to data-intensive systems.</li>
|
||
</ul>
|
||
<h3 id="streaming">Streaming<a class="headerlink" href="#streaming" title="Permanent link">¶</a></h3>
|
||
<h4 id="kafka">Kafka<a class="headerlink" href="#kafka" title="Permanent link">¶</a></h4>
|
||
<ul>
|
||
<li><strong>(2021)</strong> <a href="https://towardsdatascience.com/using-kafka-with-avro-in-python-da85b3e0f966">towardsdatascience.com: Schemafull streaming data processing in ML pipelines</a> <span class='md-tag md-tag--warning'>[PYTHON CONTENT]</span> <span class='md-tag md-tag--critical'>[ADVANCED LEVEL]</span> 🌟🌟🌟 <span class='md-tag md-tag--info'>[COMMUNITY-TOOL]</span> — Technical analysis of schema-driven streaming pipelines using Apache Kafka and Apache Avro in Python. Demonstrates how strict schema enforcement prevents downstream ML model ingestion errors. Crucial for designing real-time feature stores and maintaining strong structural contracts across distributed data microservices.</li>
|
||
</ul>
|
||
<h2 id="data-science">Data Science<a class="headerlink" href="#data-science" title="Permanent link">¶</a></h2>
|
||
<h3 id="career-guidance">Career Guidance<a class="headerlink" href="#career-guidance" title="Permanent link">¶</a></h3>
|
||
<h4 id="industry-trends">Industry Trends<a class="headerlink" href="#industry-trends" title="Permanent link">¶</a></h4>
|
||
<ul>
|
||
<li><strong>(2021)</strong> <a href="https://analyticsindiamag.com/is-coding-necessary-to-work-as-a-data-scientist">analyticsindiamag.com: Is coding necessary to work as a data scientist?</a> <span class='md-tag md-tag--warning'>[NONE CONTENT]</span> 🌟🌟 <span class='md-tag md-tag--info'>[COMMUNITY-TOOL]</span> — Analyzes the tension between low-code/no-code ML frameworks and custom code solutions. Synthesizes why advanced programming remains essential for architectural optimization, pipeline reliability, custom deployment debugging, and deep system engineering.</li>
|
||
</ul>
|
||
<h3 id="cloud-notebooks">Cloud Notebooks<a class="headerlink" href="#cloud-notebooks" title="Permanent link">¶</a></h3>
|
||
<h4 id="data-engineering-1">Data Engineering (1)<a class="headerlink" href="#data-engineering-1" title="Permanent link">¶</a></h4>
|
||
<ul>
|
||
<li><strong>(2022)</strong> <a href="https://www.freecodecamp.org/news/how-to-download-kaggle-dataset-to-google-colab">freecodecamp.org: How to Download a Kaggle Dataset Directly to a Google Colab Notebook</a> <span class='md-tag md-tag--warning'>[PYTHON CONTENT]</span> <span class='md-tag md-tag--info'>[COMMUNITY-TOOL]</span> — A practical walk-through detailing how to pull datasets from Kaggle directly into Google Colab instances via the command line. This method bypasses local download bottlenecks, optimizing workspace staging workflows.</li>
|
||
</ul>
|
||
<h3 id="data-frames">Data Frames<a class="headerlink" href="#data-frames" title="Permanent link">¶</a></h3>
|
||
<h4 id="out-of-core-processing">Out-of-Core Processing<a class="headerlink" href="#out-of-core-processing" title="Permanent link">¶</a></h4>
|
||
<ul>
|
||
<li><strong>(2023)</strong> <a href="https://vaex.io"><strong>vaex.io</strong></a> <span class='md-tag md-tag--warning'>[PYTHON CONTENT]</span> 🌟🌟🌟🌟 <span class='md-tag md-tag--success'>[ENTERPRISE-STABLE]</span> — A highly efficient out-of-core DataFrame library designed to analyze, visualize, and map massive tabular datasets containing billions of rows. By using memory mapping and zero-copy concepts, it executes complex computations without exhausting local RAM.</li>
|
||
</ul>
|
||
<h3 id="datasets">Datasets<a class="headerlink" href="#datasets" title="Permanent link">¶</a></h3>
|
||
<h4 id="medical-imaging">Medical Imaging<a class="headerlink" href="#medical-imaging" title="Permanent link">¶</a></h4>
|
||
<ul>
|
||
<li><strong>(2024)</strong> <a href="https://www.isic-archive.com">isic-archive.com</a> <span class='md-tag md-tag--warning'>[NONE CONTENT]</span> <span class='md-tag md-tag--primary'>[DOCUMENTATION]</span> <span class='md-tag md-tag--info'>[COMMUNITY-TOOL]</span> — The International Skin Imaging Collaboration (ISIC) hosts the world's largest open clinical image archive for dermatological AI research. This platform is a critical resource for clinical validation, transfer learning, and training diagnostic computer vision architectures.</li>
|
||
</ul>
|
||
<h4 id="regression-analysis">Regression Analysis<a class="headerlink" href="#regression-analysis" title="Permanent link">¶</a></h4>
|
||
<ul>
|
||
<li><strong>(2023)</strong> <a href="https://www.kaggle.com/datasets/rkiattisak/sports-car-prices-dataset">kaggle.com: Sports Car Prices dataset</a> <span class='md-tag md-tag--warning'>[NONE CONTENT]</span> <span class='md-tag md-tag--primary'>[DOCUMENTATION]</span> <span class='md-tag md-tag--info'>[COMMUNITY-TOOL]</span> — A multivariate tabular dataset cataloging sports car configurations alongside retail pricing. Ideal for baseline testing of regression models, feature encoding exercises, and setting up clean tabular learning pipelines.</li>
|
||
</ul>
|
||
<h3 id="developer-tooling">Developer Tooling<a class="headerlink" href="#developer-tooling" title="Permanent link">¶</a></h3>
|
||
<h4 id="python-ecosystem">Python Ecosystem<a class="headerlink" href="#python-ecosystem" title="Permanent link">¶</a></h4>
|
||
<ul>
|
||
<li><strong>(2023)</strong> <a href="https://thenewstack.io/7-must-have-python-tools-for-ml-devs-and-data-scientists">thenewstack.io: 7 Must-Have Python Tools for ML Devs and Data Scientists 🌟</a> <span class='md-tag md-tag--warning'>[PYTHON CONTENT]</span> <span class='md-tag md-tag--info'>[COMMUNITY-TOOL]</span> — A technical article highlighting the top Python tools and libraries that modern data scientists use to build and optimize their models. It evaluates features across libraries focused on performance, profiling, testing, and memory usage.</li>
|
||
</ul>
|
||
<h2 id="data-version-control">Data Version Control<a class="headerlink" href="#data-version-control" title="Permanent link">¶</a></h2>
|
||
<h3 id="developer-tooling-1">Developer Tooling (1)<a class="headerlink" href="#developer-tooling-1" title="Permanent link">¶</a></h3>
|
||
<ul>
|
||
<li><strong>(2024)</strong> <a href="https://marketplace.visualstudio.com/items?itemName=Iterative.dvc"><strong>DVC</strong></a> <span class='md-tag md-tag--warning'>[TYPESCRIPT CONTENT]</span> 🌟🌟🌟🌟 <span class='md-tag md-tag--success'>[ENTERPRISE-STABLE]</span> — The VS Code extension for Data Version Control (DVC) enables developers to view experiments, visual graphs, and dataset versions natively from their editor. By simplifying visual pipelines, it streamlines collaborative feature engineering and reproducible experimentation.</li>
|
||
<li><strong>(2023)</strong> <a href="https://learn.microsoft.com/en-us/shows/vs-code-livestreams/machine-learning-experimentation-in-vs-code-with-dvc-extension">docs.microsoft.com: Machine Learning Experimentation in VS Code with DVC Extension</a> <span class='md-tag md-tag--warning'>[NONE CONTENT]</span> <span class='md-tag md-tag--primary'>[DOCUMENTATION]</span> <span class='md-tag md-tag--info'>[COMMUNITY-TOOL]</span> — A video walk-through and documentation highlighting DVC-based model experimentation processes in VS Code. Shows how database tracking and Git-based data configuration workflows are visualised, resolving typical data-drift tracking issues.</li>
|
||
</ul>
|
||
<h2 id="deployment">Deployment<a class="headerlink" href="#deployment" title="Permanent link">¶</a></h2>
|
||
<h3 id="kubernetes-orchestration">Kubernetes Orchestration<a class="headerlink" href="#kubernetes-orchestration" title="Permanent link">¶</a></h3>
|
||
<ul>
|
||
<li><strong>(2022)</strong> <a href="https://github.com/bodywork-ml/bodywork-core">bodywork-ml/bodywork-core: Bodywork</a> <span class='md-tag md-tag--info'>⭐ 436</span> <svg class="v2-sparkline" width="50" height="15" viewBox="0 0 50 15" style="vertical-align: middle; display: inline-block; margin-left: 6px;" title="Activity Trend"><defs><linearGradient id="spark-grad-80063ec9" x1="0" y1="0" x2="1" y2="0"><stop offset="0%" stop-color="rgba(34, 211, 238, 0.2)" /><stop offset="100%" stop-color="var(--md-accent-fg-color)" /></linearGradient></defs><path class="v2-sparkline-path" d="M 0 5 L 10 6 L 20 7 L 30 13 L 40 12 L 50 3" fill="none" stroke="url(#spark-grad-80063ec9)" stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round" /><circle cx="50" cy="3" r="2" fill="var(--md-accent-fg-color)" /></svg> <span class='md-tag md-tag--warning'>[PYTHON CONTENT]</span> 🌟🌟 <span class='md-tag md-tag--info'>[COMMUNITY-TOOL]</span> — Bodywork acts as a pipeline orchestrator and deployment tool focused on shipping machine learning systems directly into Kubernetes. While currently seeing low developer activity, it remains a valuable conceptual blueprint for running serverless, stateful, and batch-oriented ML pipelines.</li>
|
||
</ul>
|
||
<h2 id="development-environments">Development Environments<a class="headerlink" href="#development-environments" title="Permanent link">¶</a></h2>
|
||
<h3 id="containerization">Containerization<a class="headerlink" href="#containerization" title="Permanent link">¶</a></h3>
|
||
<ul>
|
||
<li><strong>(2023)</strong> <a href="https://github.com/tensorchord/envd">tensorchord/envd: Reproducible development environment for AI/ML 🌟</a> <span class='md-tag md-tag--info'>⭐ 2209</span> <svg class="v2-sparkline" width="50" height="15" viewBox="0 0 50 15" style="vertical-align: middle; display: inline-block; margin-left: 6px;" title="Activity Trend"><defs><linearGradient id="spark-grad-92382e63" x1="0" y1="0" x2="1" y2="0"><stop offset="0%" stop-color="rgba(34, 211, 238, 0.2)" /><stop offset="100%" stop-color="var(--md-accent-fg-color)" /></linearGradient></defs><path class="v2-sparkline-path" d="M 0 12 L 10 13 L 20 7 L 30 4 L 40 12 L 50 5" fill="none" stroke="url(#spark-grad-92382e63)" stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round" /><circle cx="50" cy="5" r="2" fill="var(--md-accent-fg-color)" /></svg> <span class='md-tag md-tag--warning'>[GO CONTENT]</span> 🌟🌟🌟 <span class='md-tag md-tag--info'>[COMMUNITY-TOOL]</span> — An innovative local development environment generator for ML engineering. Envd translates Python declarations into isolated container definitions, ensuring high reproducibility for CUDA packages, pip dependency trees, and IDE plugins without writing complex Dockerfiles.</li>
|
||
</ul>
|
||
<h2 id="distributed-systems">Distributed Systems<a class="headerlink" href="#distributed-systems" title="Permanent link">¶</a></h2>
|
||
<h3 id="compute-engines">Compute Engines<a class="headerlink" href="#compute-engines" title="Permanent link">¶</a></h3>
|
||
<h4 id="ray">Ray<a class="headerlink" href="#ray" title="Permanent link">¶</a></h4>
|
||
<ul>
|
||
<li><strong>(2026)</strong> <a href="https://docs.ray.io/en/latest"><mark>Ray</mark></a> <span class='md-tag md-tag--warning'>[C++ CONTENT]</span> <span class='md-tag md-tag--critical'>[ADVANCED LEVEL]</span> <span class='md-tag md-tag--primary'>[DOCUMENTATION]</span> 🌟🌟🌟🌟🌟 <span class='md-tag md-tag--success'>[DE FACTO STANDARD]</span> — Ray is the premier distributed execution framework for scaling compute-heavy AI and Python workloads. It provides low-overhead, dynamic actor execution models, powering distributed training (Ray Train), hyperparameter tuning (Ray Tune), and model serving (Ray Serve) at enterprise scale.</li>
|
||
</ul>
|
||
<h2 id="experiment-tracking">Experiment Tracking<a class="headerlink" href="#experiment-tracking" title="Permanent link">¶</a></h2>
|
||
<h3 id="visualization">Visualization<a class="headerlink" href="#visualization" title="Permanent link">¶</a></h3>
|
||
<ul>
|
||
<li><strong>(2024)</strong> <a href="https://github.com/aimhubio/aim"><strong>github.com/aimhubio/aim</strong></a> <span class='md-tag md-tag--info'>⭐ 6154</span> <svg class="v2-sparkline" width="50" height="15" viewBox="0 0 50 15" style="vertical-align: middle; display: inline-block; margin-left: 6px;" title="Activity Trend"><defs><linearGradient id="spark-grad-6cae91fa" x1="0" y1="0" x2="1" y2="0"><stop offset="0%" stop-color="rgba(34, 211, 238, 0.2)" /><stop offset="100%" stop-color="var(--md-accent-fg-color)" /></linearGradient></defs><path class="v2-sparkline-path" d="M 0 4 L 10 2 L 20 9 L 30 3 L 40 6 L 50 3" fill="none" stroke="url(#spark-grad-6cae91fa)" stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round" /><circle cx="50" cy="3" r="2" fill="var(--md-accent-fg-color)" /></svg> <span class='md-tag md-tag--warning'>[PYTHON CONTENT]</span> 🌟🌟🌟🌟 <span class='md-tag md-tag--success'>[ENTERPRISE-STABLE]</span> — Aim is an open-source, highly responsive experiment tracking and visualization dashboard for machine learning. It provides a robust query language and a user-friendly UI to compare thousands of metrics, hyperparameters, and logs across deep learning runs.</li>
|
||
</ul>
|
||
<h2 id="generative-ai">Generative AI<a class="headerlink" href="#generative-ai" title="Permanent link">¶</a></h2>
|
||
<h3 id="distributed-systems-1">Distributed Systems (1)<a class="headerlink" href="#distributed-systems-1" title="Permanent link">¶</a></h3>
|
||
<h4 id="case-studies">Case Studies<a class="headerlink" href="#case-studies" title="Permanent link">¶</a></h4>
|
||
<ul>
|
||
<li><strong>(2023)</strong> <a href="https://www.youtube.com/watch?v=-sZvzW40NrM&ab_channel=Anyscale"><strong>youtube.com: Optimizing LLM Training with Airbnb's Next-Gen ML Platform</strong></a> <span class='md-tag md-tag--warning'>[NONE CONTENT]</span> <span class='md-tag md-tag--critical'>[ADVANCED LEVEL]</span> 🌟🌟🌟🌟 <span class='md-tag md-tag--success'>[ENTERPRISE-STABLE]</span> — Technical presentation on scaling Large Language Model (LLM) fine-tuning pipelines using Airbnb's state-of-the-art ML platform. Deep dives into high-performance distributed training clusters, dynamic resource balancing, and optimizations implemented to overcome GPU memory scaling bottlenecks.</li>
|
||
</ul>
|
||
<h3 id="llm-ops">LLM Ops<a class="headerlink" href="#llm-ops" title="Permanent link">¶</a></h3>
|
||
<h4 id="aws-1">AWS (1)<a class="headerlink" href="#aws-1" title="Permanent link">¶</a></h4>
|
||
<ul>
|
||
<li><strong>(2023)</strong> <a href="https://towardsdatascience.com/deploying-llm-apps-to-aws-the-open-source-self-service-way-c54b8667d829"><strong>towardsdatascience.com: Deploying LLM Apps to AWS, the Open-Source Self-Service Way</strong></a> <span class='md-tag md-tag--warning'>[PYTHON CONTENT]</span> <span class='md-tag md-tag--critical'>[ADVANCED LEVEL]</span> 🌟🌟🌟🌟 <span class='md-tag md-tag--success'>[ENTERPRISE-STABLE]</span> — Presents a self-service architectural framework for deploying LLM microservices to AWS with open-source infrastructure-as-code tools. Outlines the provisioning of specialized GPU-backed instances, serverless scaling mechanics, and custom embedding cache deployments to balance performance with operating costs.</li>
|
||
</ul>
|
||
<h4 id="self-assessment">Self-Assessment<a class="headerlink" href="#self-assessment" title="Permanent link">¶</a></h4>
|
||
<ul>
|
||
<li><strong>(2023)</strong> <a href="https://aiml.com/quizzes/deep-learning-large-language-models-quiz-medium">aiml.com: Large Language Models Quiz (Medium)</a> <span class='md-tag md-tag--warning'>[NONE CONTENT]</span> 🌟🌟 <span class='md-tag md-tag--info'>[COMMUNITY-TOOL]</span> — A structured self-assessment covering core concepts of Transformer architectures, attention layers, tokenization mechanisms, and transfer learning fundamentals. Designed to benchmark engineering comprehension of large language model internal mechanics.</li>
|
||
</ul>
|
||
<h4 id="system-design-1">System Design (1)<a class="headerlink" href="#system-design-1" title="Permanent link">¶</a></h4>
|
||
<ul>
|
||
<li><strong>(2023)</strong> <a href="https://huyenchip.com/2023/04/11/llm-engineering.html"><mark>huyenchip.com: Building LLM applications for production</mark></a> <span class='md-tag md-tag--warning'>[NONE CONTENT]</span> <span class='md-tag md-tag--critical'>[ADVANCED LEVEL]</span> 🌟🌟🌟🌟🌟 <span class='md-tag md-tag--success'>[DE FACTO STANDARD]</span> — A seminal, highly-cited framework for deploying Large Language Model applications in production environments. Addresses technical hurdles such as context window management, prompting reliability, latency optimization, cost-efficiency trade-offs, and structural output sanitization. Essential reading for modern generative AI architects.</li>
|
||
</ul>
|
||
<h2 id="governance">Governance<a class="headerlink" href="#governance" title="Permanent link">¶</a></h2>
|
||
<h3 id="maturity-models">Maturity Models<a class="headerlink" href="#maturity-models" title="Permanent link">¶</a></h3>
|
||
<ul>
|
||
<li><strong>(2022)</strong> <a href="https://learn.microsoft.com/en-us/azure/architecture/ai-ml/guide/mlops-maturity-model"><strong>learn.microsoft.com: Machine Learning operations maturity model 🌟</strong></a> <span class='md-tag md-tag--warning'>[NONE CONTENT]</span> <span class='md-tag md-tag--critical'>[ADVANCED LEVEL]</span> 🌟🌟🌟🌟 <span class='md-tag md-tag--success'>[ENTERPRISE-STABLE]</span> — Detail-rich architectural framework defining the five levels of MLOps organizational maturity. Translates nebulous operational tasks into concrete capabilities encompassing model versioning, automated deployments, feature stores, drift alerts, and cross-functional continuous feedback loops.</li>
|
||
</ul>
|
||
<h2 id="infrastructure">Infrastructure<a class="headerlink" href="#infrastructure" title="Permanent link">¶</a></h2>
|
||
<h3 id="container-runtimes">Container Runtimes<a class="headerlink" href="#container-runtimes" title="Permanent link">¶</a></h3>
|
||
<h4 id="gpu-drivers">GPU Drivers<a class="headerlink" href="#gpu-drivers" title="Permanent link">¶</a></h4>
|
||
<ul>
|
||
<li><strong>(2024)</strong> <a href="https://nix.dev/manual/nix/2.28">Nix</a> <span class='md-tag md-tag--warning'>[GO CONTENT]</span> <span class='md-tag md-tag--critical'>[ADVANCED LEVEL]</span> <span class='md-tag md-tag--primary'>[DOCUMENTATION]</span> <span class='md-tag md-tag--info'>[COMMUNITY-TOOL]</span> — This reference analysis investigates how nvidia-docker mounts and exposes host driver layers to application runtimes. By diving directly into the underlying Go implementation, it uncovers runtime volume mounting patterns that official NVIDIA documentation often obscures.</li>
|
||
</ul>
|
||
<h3 id="gpu-monitoring">GPU Monitoring<a class="headerlink" href="#gpu-monitoring" title="Permanent link">¶</a></h3>
|
||
<h4 id="command-line-tools">Command Line Tools<a class="headerlink" href="#command-line-tools" title="Permanent link">¶</a></h4>
|
||
<ul>
|
||
<li><strong>(2024)</strong> <a href="https://github.com/XuehaiPan/nvitop"><strong>github.com/XuehaiPan/nvitop 🌟</strong></a> <span class='md-tag md-tag--info'>⭐ 6957</span> <svg class="v2-sparkline" width="50" height="15" viewBox="0 0 50 15" style="vertical-align: middle; display: inline-block; margin-left: 6px;" title="Activity Trend"><defs><linearGradient id="spark-grad-f04e6f82" x1="0" y1="0" x2="1" y2="0"><stop offset="0%" stop-color="rgba(34, 211, 238, 0.2)" /><stop offset="100%" stop-color="var(--md-accent-fg-color)" /></linearGradient></defs><path class="v2-sparkline-path" d="M 0 8 L 10 11 L 20 11 L 30 2 L 40 12 L 50 4" fill="none" stroke="url(#spark-grad-f04e6f82)" stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round" /><circle cx="50" cy="4" r="2" fill="var(--md-accent-fg-color)" /></svg> <span class='md-tag md-tag--warning'>[PYTHON CONTENT]</span> 🌟🌟🌟🌟 <span class='md-tag md-tag--success'>[ENTERPRISE-STABLE]</span> — An interactive, terminal-based GPU monitoring tool that acts as a modern replacement for nvidia-smi. It provides real-time tracking of GPU resource consumption, memory configurations, process owners, and historical usage diagrams directly in the shell.</li>
|
||
</ul>
|
||
<h3 id="gpu-orchestration">GPU Orchestration<a class="headerlink" href="#gpu-orchestration" title="Permanent link">¶</a></h3>
|
||
<h4 id="kubernetes-operators">Kubernetes Operators<a class="headerlink" href="#kubernetes-operators" title="Permanent link">¶</a></h4>
|
||
<ul>
|
||
<li><strong>(2024)</strong> <a href="https://catalog.ngc.nvidia.com/orgs/nvidia/helm-charts/gpu-operator"><mark>catalog.ngc.nvidia.com: NVIDIA GPU Operator - Helm chart 🌟🌟🌟</mark></a> <span class='md-tag md-tag--warning'>[GO CONTENT]</span> <span class='md-tag md-tag--critical'>[ADVANCED LEVEL]</span> 🌟🌟🌟🌟🌟 <span class='md-tag md-tag--success'>[DE FACTO STANDARD]</span> — The official NVIDIA GPU Operator Helm Chart coordinates all physical driver configurations, container engine runtimes, device plugins, and monitoring layers on Kubernetes. This is the industry-standard approach to automated provisioning of GPU compute capabilities across massive cloud and on-premise clusters.</li>
|
||
</ul>
|
||
<h4 id="kubernetes-setup">Kubernetes Setup<a class="headerlink" href="#kubernetes-setup" title="Permanent link">¶</a></h4>
|
||
<ul>
|
||
<li><strong>(2023)</strong> <a href="https://www.jimangel.io/posts/nvidia-rtx-gpu-kubernetes-setup">jimangel.io: A Practical Guide to Running NVIDIA GPUs on Kubernetes</a> <span class='md-tag md-tag--warning'>[NONE CONTENT]</span> <span class='md-tag md-tag--info'>[COMMUNITY-TOOL]</span> — A hands-on, practical engineering guide details running native NVIDIA RTX graphics units within a custom-built Kubernetes cluster. It walks through low-level container runtime configurations, containerd settings, and the validation steps required for robust resource scheduling.</li>
|
||
</ul>
|
||
<h4 id="nix-package-manager">Nix Package Manager<a class="headerlink" href="#nix-package-manager" title="Permanent link">¶</a></h4>
|
||
<ul>
|
||
<li><strong>(2023)</strong> <a href="https://www.canva.dev/blog/engineering/supporting-gpu-accelerated-machine-learning-with-kubernetes-and-nix">canvatechblog.com: Supporting GPU-accelerated Machine Learning with Kubernetes and Nix</a> <span class='md-tag md-tag--warning'>[NIX CONTENT]</span> <span class='md-tag md-tag--critical'>[ADVANCED LEVEL]</span> <span class='md-tag md-tag--info'>[COMMUNITY-TOOL]</span> — An in-depth engineering review from Canva detailing their architectural transition to using Nix for managing complex, GPU-accelerated machine learning workloads inside Kubernetes. It addresses the limits of typical container images by relying on Nix to guarantee deterministic and highly reproducible C-library and CUDA dependencies.</li>
|
||
</ul>
|
||
<h4 id="resource-allocation">Resource Allocation<a class="headerlink" href="#resource-allocation" title="Permanent link">¶</a></h4>
|
||
<ul>
|
||
<li><strong>(2024)</strong> <a href="https://huggingface.co/blog/NileshInfer/implementing-fractional-gpus-in-kubernetes">huggingface.co: Implementing Fractional GPUs in Kubernetes with Aliyun Scheduler</a> <span class='md-tag md-tag--warning'>[GO CONTENT]</span> <span class='md-tag md-tag--critical'>[ADVANCED LEVEL]</span> <span class='md-tag md-tag--info'>[COMMUNITY-TOOL]</span> — A deep dive into fractional GPU scheduling policies on Kubernetes using the open-source Aliyun Scheduler. It showcases strategies for maximizing resource utility and lowering infrastructure bills by sharing single physical hardware resources across smaller model-serving pods.</li>
|
||
</ul>
|
||
<h2 id="kubernetes">Kubernetes<a class="headerlink" href="#kubernetes" title="Permanent link">¶</a></h2>
|
||
<h3 id="architectural-patterns">Architectural Patterns<a class="headerlink" href="#architectural-patterns" title="Permanent link">¶</a></h3>
|
||
<ul>
|
||
<li><strong>(2021)</strong> <a href="https://towardsdatascience.com/a-kubernetes-architecture-for-machine-learning-web-application-deployments-632f7765ef29">towardsdatascience.com: A Kubernetes architecture for machine learning web-application deployments</a> <span class='md-tag md-tag--warning'>[YAML CONTENT]</span> <span class='md-tag md-tag--critical'>[ADVANCED LEVEL]</span> 🌟🌟🌟 <span class='md-tag md-tag--info'>[COMMUNITY-TOOL]</span> — Outlines a highly resilient architectural blueprint for deploying machine learning models on Kubernetes. Discusses containerizing model APIs, managing resource limits, utilizing ingress controllers, and decoupling frontend services from computational inference backends. Offers concrete patterns for scaling web apps backed by heavy-weight deep learning payloads.</li>
|
||
</ul>
|
||
<h3 id="artifact-registration">Artifact Registration<a class="headerlink" href="#artifact-registration" title="Permanent link">¶</a></h3>
|
||
<ul>
|
||
<li><strong>(2023)</strong> <a href="https://artifacthub.io/packages/helm/mlflowserver/mlflow-server">artifacthub.io: mlflow-server</a> <span class='md-tag md-tag--warning'>[YAML CONTENT]</span> <span class='md-tag md-tag--critical'>[ADVANCED LEVEL]</span> 🌟🌟🌟 <span class='md-tag md-tag--info'>[COMMUNITY-TOOL]</span> — Official or community Helm Chart designed to bootstrap a highly available MLflow tracking and registry server inside a Kubernetes cluster. Streamlines configuring databases, AWS S3 / MinIO backend stores, and ingress mechanisms required for cloud-native model lifecycle management.</li>
|
||
</ul>
|
||
<h3 id="component-engineering">Component Engineering<a class="headerlink" href="#component-engineering" title="Permanent link">¶</a></h3>
|
||
<ul>
|
||
<li><strong>(2021)</strong> <a href="https://itnext.io/building-ml-componentes-on-kubernetes-fc7e24cb9269">itnext.io: Building ML Componentes on Kubernetes</a> <span class='md-tag md-tag--warning'>[GO CONTENT]</span> <span class='md-tag md-tag--critical'>[ADVANCED LEVEL]</span> 🌟🌟🌟 <span class='md-tag md-tag--info'>[COMMUNITY-TOOL]</span> — A deep dive into structuring modular machine learning pipeline components inside a Kubernetes cluster. Focuses on orchestrating stateless compute workloads, defining clear volume interfaces, and managing persistent training artifacts. Highly relevant for architects planning custom infrastructure abstractions over vanilla K8s primitives.</li>
|
||
</ul>
|
||
<h3 id="deployment-guides">Deployment Guides<a class="headerlink" href="#deployment-guides" title="Permanent link">¶</a></h3>
|
||
<ul>
|
||
<li><strong>(2023)</strong> <a href="https://dev.to/pavanbelagatti/deploy-any-aiml-application-on-kubernetes-a-step-by-step-guide-2i37">dev.to/pavanbelagatti: Deploy Any AI/ML Application On Kubernetes: A Step-by-Step Guide!</a> <span class='md-tag md-tag--warning'>[YAML CONTENT]</span> 🌟🌟🌟 <span class='md-tag md-tag--info'>[COMMUNITY-TOOL]</span> — Hands-on guide showing how to package, deploy, and scale diverse machine learning applications using Kubernetes manifests. Focuses on establishing proper ingress routing, service definitions, CPU/GPU resource constraints, and continuous monitoring sidecars within a native cluster environment.</li>
|
||
</ul>
|
||
<h3 id="kubeflow">Kubeflow<a class="headerlink" href="#kubeflow" title="Permanent link">¶</a></h3>
|
||
<ul>
|
||
<li><strong>(2021)</strong> <a href="https://www.infracloud.io/blogs/machine-learning-orchestration-kubernetes-kubeflow">infracloud.io: Machine Learning Orchestration on Kubernetes using Kubeflow</a> <span class='md-tag md-tag--warning'>[NONE CONTENT]</span> 🌟🌟🌟 <span class='md-tag md-tag--info'>[COMMUNITY-TOOL]</span> — Explores the practical orchestration of complex ML workflows using Kubeflow pipelines on Kubernetes. Outlines the underlying architecture, components (e.g., pipelines, notebook servers, metadata), and strategic advantages over non-containerized distributed ML setups.</li>
|
||
</ul>
|
||
<h2 id="learning-roadmap-1">Learning Roadmap (1)<a class="headerlink" href="#learning-roadmap-1" title="Permanent link">¶</a></h2>
|
||
<h3 id="courses">Courses<a class="headerlink" href="#courses" title="Permanent link">¶</a></h3>
|
||
<ul>
|
||
<li><strong>(2023)</strong> <a href="https://www.freecodecamp.org/news/mlops-course-learn-to-build-machine-learning-production-grade-projects"><strong>freecodecamp.org: MLOps Course – Learn to Build Machine Learning Production Grade Projects</strong></a> <span class='md-tag md-tag--warning'>[PYTHON CONTENT]</span> 🌟🌟🌟🌟 <span class='md-tag md-tag--success'>[ENTERPRISE-STABLE]</span> <span class='md-tag md-tag--secondary'>[GUIDE]</span> — A complete, production-grade syllabus designed to guide engineers through model training, automated deployment strategies, CI/CD, and system monitoring. Emphasizes building production-ready architectures with clear structural boundaries rather than pure algorithmic modeling.</li>
|
||
</ul>
|
||
<h3 id="curated-reference">Curated Reference<a class="headerlink" href="#curated-reference" title="Permanent link">¶</a></h3>
|
||
<ul>
|
||
<li><strong>(2024)</strong> <a href="https://roadmap.sh/r?id=65a112f2b8633950ffcf38b6"><mark>roadmap.sh: MLOps roadmap</mark></a> <span class='md-tag md-tag--warning'>[NONE CONTENT]</span> 🌟🌟🌟🌟🌟 <span class='md-tag md-tag--success'>[DE FACTO STANDARD]</span> — An interactive, structured roadmap detailing the precise skills, methodologies, and technologies required to master modern MLOps. Covers foundational software engineering, distributed system design, data pipelines, model orchestration, and telemetry architectures.</li>
|
||
</ul>
|
||
<h2 id="machine-learning-1">Machine Learning (1)<a class="headerlink" href="#machine-learning-1" title="Permanent link">¶</a></h2>
|
||
<h3 id="competitions">Competitions<a class="headerlink" href="#competitions" title="Permanent link">¶</a></h3>
|
||
<h4 id="datasets-1">Datasets (1)<a class="headerlink" href="#datasets-1" title="Permanent link">¶</a></h4>
|
||
<ul>
|
||
<li><strong>(2024)</strong> <a href="https://www.kaggle.com/competitions"><mark>Kaggle Competitions</mark></a> <span class='md-tag md-tag--warning'>[NONE CONTENT]</span> <span class='md-tag md-tag--primary'>[DOCUMENTATION]</span> 🌟🌟🌟🌟🌟 <span class='md-tag md-tag--success'>[DE FACTO STANDARD]</span> <span class='md-tag md-tag--warning'>[EMERGING]</span> — Kaggle stands as the premier community catalog and competition hub for data science. It enables engineers to extract real-world datasets, benchmark their model configurations, and leverage managed GPU runtimes for experimental validation.</li>
|
||
</ul>
|
||
<h3 id="computer-vision">Computer Vision<a class="headerlink" href="#computer-vision" title="Permanent link">¶</a></h3>
|
||
<h4 id="instance-segmentation">Instance Segmentation<a class="headerlink" href="#instance-segmentation" title="Permanent link">¶</a></h4>
|
||
<ul>
|
||
<li><strong>(2023)</strong> <a href="https://github.com/CASIA-LMC-Lab/FastSAM"><strong>github.com/CASIA-IVA-Lab/FastSAM</strong></a> <span class='md-tag md-tag--info'>⭐ 8364</span> <svg class="v2-sparkline" width="50" height="15" viewBox="0 0 50 15" style="vertical-align: middle; display: inline-block; margin-left: 6px;" title="Activity Trend"><defs><linearGradient id="spark-grad-84edd76e" x1="0" y1="0" x2="1" y2="0"><stop offset="0%" stop-color="rgba(34, 211, 238, 0.2)" /><stop offset="100%" stop-color="var(--md-accent-fg-color)" /></linearGradient></defs><path class="v2-sparkline-path" d="M 0 12 L 10 6 L 20 6 L 30 8 L 40 10 L 50 5" fill="none" stroke="url(#spark-grad-84edd76e)" stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round" /><circle cx="50" cy="5" r="2" fill="var(--md-accent-fg-color)" /></svg> <span class='md-tag md-tag--warning'>[PYTHON CONTENT]</span> <span class='md-tag md-tag--critical'>[ADVANCED LEVEL]</span> 🌟🌟🌟🌟 <span class='md-tag md-tag--success'>[ENTERPRISE-STABLE]</span> — Fast SAM offers a highly optimized, CNN-based real-time alternative to Meta's Segment Anything Model. By sacrificing minimal accuracy, it reduces latency and computation footprints, which is critical for edge deployments and microservice image APIs.</li>
|
||
</ul>
|
||
<h3 id="databases">Databases<a class="headerlink" href="#databases" title="Permanent link">¶</a></h3>
|
||
<h4 id="in-database-ml">In-database ML<a class="headerlink" href="#in-database-ml" title="Permanent link">¶</a></h4>
|
||
<ul>
|
||
<li><strong>(2024)</strong> <a href="https://github.com/postgresml/postgresml"><strong>postgresml/postgresml 🌟</strong></a> <span class='md-tag md-tag--info'>⭐ 6800</span> <svg class="v2-sparkline" width="50" height="15" viewBox="0 0 50 15" style="vertical-align: middle; display: inline-block; margin-left: 6px;" title="Activity Trend"><defs><linearGradient id="spark-grad-5910dbbe" x1="0" y1="0" x2="1" y2="0"><stop offset="0%" stop-color="rgba(34, 211, 238, 0.2)" /><stop offset="100%" stop-color="var(--md-accent-fg-color)" /></linearGradient></defs><path class="v2-sparkline-path" d="M 0 4 L 10 7 L 20 13 L 30 13 L 40 8 L 50 4" fill="none" stroke="url(#spark-grad-5910dbbe)" stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round" /><circle cx="50" cy="4" r="2" fill="var(--md-accent-fg-color)" /></svg> <span class='md-tag md-tag--warning'>[RUST CONTENT]</span> <span class='md-tag md-tag--critical'>[ADVANCED LEVEL]</span> 🌟🌟🌟🌟 <span class='md-tag md-tag--success'>[ENTERPRISE-STABLE]</span> — An extension that integrates machine learning directly inside PostgreSQL, written in Rust. It enables developers to train and run real-time inference using classic models or LLMs natively through SQL, entirely bypassing external ETL and API pipeline latency.</li>
|
||
</ul>
|
||
<h3 id="distributed-training">Distributed Training<a class="headerlink" href="#distributed-training" title="Permanent link">¶</a></h3>
|
||
<h4 id="fault-tolerance">Fault Tolerance<a class="headerlink" href="#fault-tolerance" title="Permanent link">¶</a></h4>
|
||
<ul>
|
||
<li><strong>(2024)</strong> <a href="https://github.com/SymbioticLab/Oobleck">github.com/SymbioticLab/Oobleck: Oobleck - Resilient Distributed Training' Framework</a> <span class='md-tag md-tag--info'>⭐ 100</span> <svg class="v2-sparkline" width="50" height="15" viewBox="0 0 50 15" style="vertical-align: middle; display: inline-block; margin-left: 6px;" title="Activity Trend"><defs><linearGradient id="spark-grad-9476d30e" x1="0" y1="0" x2="1" y2="0"><stop offset="0%" stop-color="rgba(34, 211, 238, 0.2)" /><stop offset="100%" stop-color="var(--md-accent-fg-color)" /></linearGradient></defs><path class="v2-sparkline-path" d="M 0 3 L 10 4 L 20 10 L 30 13 L 40 13 L 50 5" fill="none" stroke="url(#spark-grad-9476d30e)" stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round" /><circle cx="50" cy="5" r="2" fill="var(--md-accent-fg-color)" /></svg> <span class='md-tag md-tag--warning'>[PYTHON CONTENT]</span> <span class='md-tag md-tag--critical'>[ADVANCED LEVEL]</span> 🌟🌟 <span class='md-tag md-tag--info'>[COMMUNITY-TOOL]</span> — An open-source distributed training framework designed with native fault-tolerant characteristics. It mitigates expensive spot-instance preemption delays by dynamically and rapidly reorganizing parallel communication pipelines on-the-fly.</li>
|
||
</ul>
|
||
<h3 id="document-analysis">Document Analysis<a class="headerlink" href="#document-analysis" title="Permanent link">¶</a></h3>
|
||
<h4 id="ocr">OCR<a class="headerlink" href="#ocr" title="Permanent link">¶</a></h4>
|
||
<ul>
|
||
<li><strong>(2024)</strong> <a href="https://github.com/datalab-to/surya"><mark>github.com/VikParuchuri/surya</mark></a> <span class='md-tag md-tag--info'>⭐ 20801</span> <svg class="v2-sparkline" width="50" height="15" viewBox="0 0 50 15" style="vertical-align: middle; display: inline-block; margin-left: 6px;" title="Activity Trend"><defs><linearGradient id="spark-grad-f39bd918" x1="0" y1="0" x2="1" y2="0"><stop offset="0%" stop-color="rgba(34, 211, 238, 0.2)" /><stop offset="100%" stop-color="var(--md-accent-fg-color)" /></linearGradient></defs><path class="v2-sparkline-path" d="M 0 6 L 10 10 L 20 6 L 30 3 L 40 11 L 50 5" fill="none" stroke="url(#spark-grad-f39bd918)" stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round" /><circle cx="50" cy="5" r="2" fill="var(--md-accent-fg-color)" /></svg> <span class='md-tag md-tag--warning'>[PYTHON CONTENT]</span> <span class='md-tag md-tag--critical'>[ADVANCED LEVEL]</span> 🌟🌟🌟🌟🌟 <span class='md-tag md-tag--success'>[DE FACTO STANDARD]</span> <span class='md-tag md-tag--critical'>[LEGACY]</span> — Surya provides multi-lingual document OCR and accurate layout analysis powered by deep learning. It delivers high-fidelity reading and structuring of dense scientific papers, tables, and financial layouts, serving as a lighter, open substitute for legacy systems.</li>
|
||
</ul>
|
||
<h3 id="education">Education<a class="headerlink" href="#education" title="Permanent link">¶</a></h3>
|
||
<h4 id="study-materials">Study Materials<a class="headerlink" href="#study-materials" title="Permanent link">¶</a></h4>
|
||
<ul>
|
||
<li><strong>(2023)</strong> <a href="https://github.com/dair-ai/ML-Course-Notes"><strong>dair-ai/ML-Course-Notes: ML Course Notes 🌟</strong></a> <span class='md-tag md-tag--info'>⭐ 6568</span> <svg class="v2-sparkline" width="50" height="15" viewBox="0 0 50 15" style="vertical-align: middle; display: inline-block; margin-left: 6px;" title="Activity Trend"><defs><linearGradient id="spark-grad-c3e96cd2" x1="0" y1="0" x2="1" y2="0"><stop offset="0%" stop-color="rgba(34, 211, 238, 0.2)" /><stop offset="100%" stop-color="var(--md-accent-fg-color)" /></linearGradient></defs><path class="v2-sparkline-path" d="M 0 3 L 10 11 L 20 12 L 30 7 L 40 6 L 50 5" fill="none" stroke="url(#spark-grad-c3e96cd2)" stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round" /><circle cx="50" cy="5" r="2" fill="var(--md-accent-fg-color)" /></svg> <span class='md-tag md-tag--warning'>[MARKDOWN CONTENT]</span> 🌟🌟🌟🌟 <span class='md-tag md-tag--success'>[ENTERPRISE-STABLE]</span> — A comprehensive collection of study notes, mathematical backgrounds, and algorithmic outlines covering modern machine learning. It is an exceptional resource for developers transitioning to production AI, offering clear reviews of model structures and deep learning theory.</li>
|
||
</ul>
|
||
<h3 id="foundations-1">Foundations (1)<a class="headerlink" href="#foundations-1" title="Permanent link">¶</a></h3>
|
||
<h4 id="scratch-implementations">Scratch Implementations<a class="headerlink" href="#scratch-implementations" title="Permanent link">¶</a></h4>
|
||
<ul>
|
||
<li><strong>(2021)</strong> <a href="https://dafriedman97.github.io/mlbook/content/introduction.html">dafriedman97.github.io: Machine Learning from Scratch</a> <span class='md-tag md-tag--warning'>[PYTHON CONTENT]</span> <span class='md-tag md-tag--info'>[COMMUNITY-TOOL]</span> — An interactive digital textbook covering foundational machine learning algorithms implemented entirely from scratch in Python. Ideal for engineers seeking underlying model math.</li>
|
||
</ul>
|
||
<h3 id="information-retrieval">Information Retrieval<a class="headerlink" href="#information-retrieval" title="Permanent link">¶</a></h3>
|
||
<h4 id="rag-pipelines">RAG Pipelines<a class="headerlink" href="#rag-pipelines" title="Permanent link">¶</a></h4>
|
||
<ul>
|
||
<li><strong>(2024)</strong> <a href="https://github.com/decodingai-magazine/articles-code/tree/main/articles/ml_system_design/real_time_news_search_with_upstash_kafka_and_vector_db">github.com/decodingml: Real-time news search engine using Upstash Kafka and Vector DB</a> <span class='md-tag md-tag--info'>⭐ 140</span> <svg class="v2-sparkline" width="50" height="15" viewBox="0 0 50 15" style="vertical-align: middle; display: inline-block; margin-left: 6px;" title="Activity Trend"><defs><linearGradient id="spark-grad-d510f1e7" x1="0" y1="0" x2="1" y2="0"><stop offset="0%" stop-color="rgba(34, 211, 238, 0.2)" /><stop offset="100%" stop-color="var(--md-accent-fg-color)" /></linearGradient></defs><path class="v2-sparkline-path" d="M 0 11 L 10 6 L 20 10 L 30 5 L 40 7 L 50 9" fill="none" stroke="url(#spark-grad-d510f1e7)" stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round" /><circle cx="50" cy="9" r="2" fill="var(--md-accent-fg-color)" /></svg> <span class='md-tag md-tag--warning'>[PYTHON CONTENT]</span> <span class='md-tag md-tag--critical'>[ADVANCED LEVEL]</span> 🌟🌟 <span class='md-tag md-tag--info'>[COMMUNITY-TOOL]</span> — A hands-on repository and architecture blueprint illustrating a real-time semantic news search engine. Combining Upstash managed Kafka for event streaming with a Vector Database for indexing, it shows how to build low-latency RAG systems.</li>
|
||
</ul>
|
||
<h3 id="large-language-models">Large Language Models<a class="headerlink" href="#large-language-models" title="Permanent link">¶</a></h3>
|
||
<h4 id="fine-tuning">Fine-tuning<a class="headerlink" href="#fine-tuning" title="Permanent link">¶</a></h4>
|
||
<ul>
|
||
<li><strong>(2023)</strong> <a href="https://github.com/meta-llama/llama-cookbook"><mark>github.com/meta-llama/llama-recipes</mark></a> <span class='md-tag md-tag--info'>⭐ 18353</span> <svg class="v2-sparkline" width="50" height="15" viewBox="0 0 50 15" style="vertical-align: middle; display: inline-block; margin-left: 6px;" title="Activity Trend"><defs><linearGradient id="spark-grad-147e5aee" x1="0" y1="0" x2="1" y2="0"><stop offset="0%" stop-color="rgba(34, 211, 238, 0.2)" /><stop offset="100%" stop-color="var(--md-accent-fg-color)" /></linearGradient></defs><path class="v2-sparkline-path" d="M 0 8 L 10 2 L 20 3 L 30 13 L 40 9 L 50 5" fill="none" stroke="url(#spark-grad-147e5aee)" stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round" /><circle cx="50" cy="5" r="2" fill="var(--md-accent-fg-color)" /></svg> <span class='md-tag md-tag--warning'>[PYTHON CONTENT]</span> <span class='md-tag md-tag--critical'>[ADVANCED LEVEL]</span> 🌟🌟🌟🌟🌟 <span class='md-tag md-tag--success'>[DE FACTO STANDARD]</span> — Meta's core repository for scaling LLM deployments. It offers highly robust templates for PEFT (Parameter-Efficient Fine-Tuning) such as LoRA, model quantization, and optimization strategies that enable low-latency inference setups inside microservices frameworks.</li>
|
||
</ul>
|
||
<h3 id="mlops">MLOps<a class="headerlink" href="#mlops" title="Permanent link">¶</a></h3>
|
||
<h4 id="model-pipelines">Model Pipelines<a class="headerlink" href="#model-pipelines" title="Permanent link">¶</a></h4>
|
||
<ul>
|
||
<li><strong>(2021)</strong> <a href="https://www.cortex.dev/post/how-to-build-a-pipeline-to-retrain-and-deploy-models">cortex.dev: How to build a pipeline to retrain and deploy models</a> <span class='md-tag md-tag--warning'>[NONE CONTENT]</span> <span class='md-tag md-tag--critical'>[ADVANCED LEVEL]</span> <span class='md-tag md-tag--critical'>[LEGACY]</span> — Outlines pipeline architectures to automate ML model retraining and deployment. Since the underlying Cortex project has been archived, it is preserved here primarily for historical MLOps framework reference.</li>
|
||
</ul>
|
||
<h3 id="medical-imaging-1">Medical Imaging (1)<a class="headerlink" href="#medical-imaging-1" title="Permanent link">¶</a></h3>
|
||
<h4 id="computer-vision-1">Computer Vision (1)<a class="headerlink" href="#computer-vision-1" title="Permanent link">¶</a></h4>
|
||
<ul>
|
||
<li><strong>(2022)</strong> <a href="https://github.com/fepegar/vesseg">fepegar/vesseg</a> <span class='md-tag md-tag--info'>⭐ 44</span> <svg class="v2-sparkline" width="50" height="15" viewBox="0 0 50 15" style="vertical-align: middle; display: inline-block; margin-left: 6px;" title="Activity Trend"><defs><linearGradient id="spark-grad-9fc7d34a" x1="0" y1="0" x2="1" y2="0"><stop offset="0%" stop-color="rgba(34, 211, 238, 0.2)" /><stop offset="100%" stop-color="var(--md-accent-fg-color)" /></linearGradient></defs><path class="v2-sparkline-path" d="M 0 8 L 10 7 L 20 9 L 30 6 L 40 11 L 50 9" fill="none" stroke="url(#spark-grad-9fc7d34a)" stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round" /><circle cx="50" cy="9" r="2" fill="var(--md-accent-fg-color)" /></svg> <span class='md-tag md-tag--warning'>[PYTHON CONTENT]</span> 🌟 <span class='md-tag md-tag--info'>[COMMUNITY-TOOL]</span> — A specialized neural segmentation repository targeting retinal vessel identification on ocular datasets. Built with PyTorch, it provides ready-to-run inference architectures, custom dataset preprocessors, and benchmarking tests tailored for ocular imaging.</li>
|
||
</ul>
|
||
<h2 id="medical-imaging-2">Medical Imaging (2)<a class="headerlink" href="#medical-imaging-2" title="Permanent link">¶</a></h2>
|
||
<h3 id="end-to-end-pipeline">End-to-End Pipeline<a class="headerlink" href="#end-to-end-pipeline" title="Permanent link">¶</a></h3>
|
||
<ul>
|
||
<li><strong>(2023)</strong> <a href="https://github.com/10tanmay100/MEDICAL-DATA-PROJECT-END2END-WITH-FEW-MLOPS">github.com/10tanmay100: MEDICAL-DATA-PROJECT-END2END-WITH-FEW-MLOPS</a> <span class='md-tag md-tag--info'>⭐ 3</span> <svg class="v2-sparkline" width="50" height="15" viewBox="0 0 50 15" style="vertical-align: middle; display: inline-block; margin-left: 6px;" title="Activity Trend"><defs><linearGradient id="spark-grad-8ced138c" x1="0" y1="0" x2="1" y2="0"><stop offset="0%" stop-color="rgba(34, 211, 238, 0.2)" /><stop offset="100%" stop-color="var(--md-accent-fg-color)" /></linearGradient></defs><path class="v2-sparkline-path" d="M 0 13 L 10 2 L 20 9 L 30 10 L 40 5 L 50 9" fill="none" stroke="url(#spark-grad-8ced138c)" stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round" /><circle cx="50" cy="9" r="2" fill="var(--md-accent-fg-color)" /></svg> <span class='md-tag md-tag--warning'>[PYTHON CONTENT]</span> 🌟 <span class='md-tag md-tag--info'>[COMMUNITY-TOOL]</span> — A template project exploring end-to-end MLOps strategies for medical imaging classifications. Built to serve as a baseline architectural guide, it shows how to parse medical image sets, structure training scripts, and deploy models as queryable endpoints.</li>
|
||
</ul>
|
||
<h2 id="model-life-cycle">Model Life Cycle<a class="headerlink" href="#model-life-cycle" title="Permanent link">¶</a></h2>
|
||
<h3 id="aws-2">AWS (2)<a class="headerlink" href="#aws-2" title="Permanent link">¶</a></h3>
|
||
<ul>
|
||
<li><strong>(2021)</strong> <a href="https://towardsdatascience.com/from-dev-to-deployment-an-end-to-end-sentiment-classifier-app-with-mlflow-sagemaker-and-119043ea4203">towardsdatascience.com: From Dev to Deployment: An End to End Sentiment Classifier App with MLflow, SageMaker, and Streamlit</a> <span class='md-tag md-tag--warning'>[PYTHON CONTENT]</span> 🌟🌟🌟 <span class='md-tag md-tag--info'>[COMMUNITY-TOOL]</span> — Walks through a complete MLOps workflow deploying a sentiment classifier. Synthesizes MLflow for parameter and metric tracking, AWS SageMaker for hosting scalable compute environments, and Streamlit for rapid front-end application prototyping. Highlights the synergy of multi-cloud and hybrid-SaaS tooling.</li>
|
||
</ul>
|
||
<h3 id="enterprise-patterns">Enterprise Patterns<a class="headerlink" href="#enterprise-patterns" title="Permanent link">¶</a></h3>
|
||
<ul>
|
||
<li><strong>(2022)</strong> <a href="https://www.union.ai/blog-post/machine-learning-in-production"><strong>Machine Learning in Production. What does an end-to-end ML workflow look like in production? (transcript) 🌟🌟🌟</strong></a> <span class='md-tag md-tag--warning'>[NONE CONTENT]</span> 🌟🌟🌟🌟 <span class='md-tag md-tag--success'>[ENTERPRISE-STABLE]</span> — A granular transcript and architectural review of an end-to-end production ML pipeline workflow. Identifies key infrastructure challenges such as reliable data ingestion, continuous model validation, distributed caching, and metadata lineage tracking.</li>
|
||
</ul>
|
||
<h2 id="model-serving-1">Model Serving (1)<a class="headerlink" href="#model-serving-1" title="Permanent link">¶</a></h2>
|
||
<h3 id="api-development">API Development<a class="headerlink" href="#api-development" title="Permanent link">¶</a></h3>
|
||
<h4 id="fastapi">FastAPI<a class="headerlink" href="#fastapi" title="Permanent link">¶</a></h4>
|
||
<ul>
|
||
<li><strong>(2021)</strong> <a href="https://towardsdatascience.com/deploying-an-ml-model-with-fastapi-a-succinct-guide-69eceda27b21"><strong>towardsdatascience.com: Deploying An ML Model With FastAPI — A Succinct Guide</strong></a> <span class='md-tag md-tag--warning'>[PYTHON CONTENT]</span> 🌟🌟🌟🌟 <span class='md-tag md-tag--success'>[ENTERPRISE-STABLE]</span> — A step-by-step technical implementation guide utilizing FastAPI for low-latency ML model serving. Highlights the benefits of asynchronous request handling, built-in Pydantic data validation, and automated OpenAPI schema generation. Demonstrates how to package the application with Docker to establish a robust microservice baseline.</li>
|
||
<li><strong>(2021)</strong> <a href="https://towardsdatascience.com/step-by-step-approach-to-build-your-machine-learning-api-using-fast-api-21bd32f2bbdb">towardsdatascience.com: Step-by-step Approach to Build Your Machine Learning API Using Fast API</a> <span class='md-tag md-tag--warning'>[PYTHON CONTENT]</span> 🌟🌟🌟 <span class='md-tag md-tag--info'>[COMMUNITY-TOOL]</span> — Practical guide outlining the architectural components needed to design an enterprise-ready FastAPI wrapper for pre-trained machine learning models. Highlights exception handling, asynchronous inference configurations, and the construction of deterministic, typed request/response contracts using Pydantic.</li>
|
||
</ul>
|
||
<h3 id="architectural-patterns-1">Architectural Patterns (1)<a class="headerlink" href="#architectural-patterns-1" title="Permanent link">¶</a></h3>
|
||
<h4 id="infrastructure-selection">Infrastructure Selection<a class="headerlink" href="#infrastructure-selection" title="Permanent link">¶</a></h4>
|
||
<ul>
|
||
<li><strong>(2024)</strong> <a href="https://www.axelmendoza.com/posts/ml-model-deployment"><strong>axelmendoza.com: The Ultimate Guide To ML Model Deployment In 2024</strong></a> <span class='md-tag md-tag--warning'>[NONE CONTENT]</span> 🌟🌟🌟🌟 <span class='md-tag md-tag--success'>[ENTERPRISE-STABLE]</span> — Comprehensive blueprint detailing contemporary paradigms of ML serving, contrasting serverless, dedicated clusters (like K8s), and edge processing. Helps infrastructure architects navigate hardware acceleration, pipeline containerization, security policies, and real-time observability structures.</li>
|
||
</ul>
|
||
<h3 id="kubernetes-1">Kubernetes (1)<a class="headerlink" href="#kubernetes-1" title="Permanent link">¶</a></h3>
|
||
<h4 id="kserve">KServe<a class="headerlink" href="#kserve" title="Permanent link">¶</a></h4>
|
||
<ul>
|
||
<li><strong>(2022)</strong> <a href="https://thenewstack.io/kserve-a-robust-and-extensible-cloud-native-model-server"><strong>thenewstack.io: KServe: A Robust and Extensible Cloud Native Model Server</strong></a> <span class='md-tag md-tag--warning'>[GO CONTENT]</span> <span class='md-tag md-tag--critical'>[ADVANCED LEVEL]</span> 🌟🌟🌟🌟 <span class='md-tag md-tag--success'>[ENTERPRISE-STABLE]</span> — Comprehensive technical exploration of KServe (formerly KFServing) on Kubernetes. Covers dynamic autoscaling (scaling down to zero via Knative), standardized ingress protocols (v2 data plane), advanced traffic routing, model validation steps, and canary rollout orchestrations.</li>
|
||
</ul>
|
||
<h3 id="microservices">Microservices<a class="headerlink" href="#microservices" title="Permanent link">¶</a></h3>
|
||
<ul>
|
||
<li><strong>(2021)</strong> <a href="https://opensource.microsoft.com/blog/2021/07/09/simple-steps-to-create-scalable-processes-to-deploy-ml-models-as-microservices">cloudblogs.microsoft.com: Simple steps to create scalable processes to deploy ML models as microservices</a> <span class='md-tag md-tag--warning'>[PYTHON CONTENT]</span> 🌟🌟🌟 <span class='md-tag md-tag--info'>[COMMUNITY-TOOL]</span> — Provides an enterprise-grade methodology for packaging machine learning models as distinct, containerized microservices. Focuses on automated CI/CD validation loops, lightweight interface design, and scalable deployment targets on Azure Kubernetes Service (AKS). Solves the organizational silo problem by treating the model as an isolated API.</li>
|
||
</ul>
|
||
<h2 id="orchestration">Orchestration<a class="headerlink" href="#orchestration" title="Permanent link">¶</a></h2>
|
||
<h3 id="airflow">Airflow<a class="headerlink" href="#airflow" title="Permanent link">¶</a></h3>
|
||
<ul>
|
||
<li><strong>(2021)</strong> <a href="https://towardsdatascience.com/build-machine-learning-pipelines-with-airflow-and-mlflow-reservation-cancellation-forecasting-da675d409842">towardsdatascience.com: Build Machine Learning Pipelines with Airflow and Mlflow: Reservation Cancellation Forecasting</a> <span class='md-tag md-tag--warning'>[PYTHON CONTENT]</span> <span class='md-tag md-tag--critical'>[ADVANCED LEVEL]</span> 🌟🌟🌟 <span class='md-tag md-tag--info'>[COMMUNITY-TOOL]</span> — Describes the convergence of Apache Airflow DAG orchestration with MLflow execution and model-tracking servers. Uses a concrete business use case (reservation forecasting) to illustrate scheduled dataset ingestion, remote training validation, and model artifact logging.</li>
|
||
</ul>
|
||
<h3 id="airflow-providers">Airflow Providers<a class="headerlink" href="#airflow-providers" title="Permanent link">¶</a></h3>
|
||
<ul>
|
||
<li><strong>(2023)</strong> <a href="https://pypi.org/project/airflow-provider-mlflow">pypi.org/project/airflow-provider-mlflow</a> <span class='md-tag md-tag--warning'>[PYTHON CONTENT]</span> 🌟🌟🌟 <span class='md-tag md-tag--info'>[COMMUNITY-TOOL]</span> — An official PyPI integration package bridging Apache Airflow workflows and MLflow experiment runs. Provides customized operators and hooks to dynamically log metrics, register models, and fetch operational parameters inside enterprise scheduled orchestration DAGs.</li>
|
||
</ul>
|
||
<h3 id="comparative-analysis">Comparative Analysis<a class="headerlink" href="#comparative-analysis" title="Permanent link">¶</a></h3>
|
||
<ul>
|
||
<li><strong>(2023)</strong> <a href="https://www.union.ai/blog-post/production-grade-ml-pipelines-flyte-vs-kubeflow"><strong>union.ai: Production-Grade ML Pipelines: Flyte™ vs. Kubeflow</strong></a> <span class='md-tag md-tag--warning'>[NONE CONTENT]</span> <span class='md-tag md-tag--critical'>[ADVANCED LEVEL]</span> 🌟🌟🌟🌟 <span class='md-tag md-tag--success'>[ENTERPRISE-STABLE]</span> — A direct, architecture-focused comparison between Flyte and Kubeflow for building enterprise-grade pipelines. Evaluates core architectural differences, trade-offs in structural state management, ease of local development, compile-time type validation, and deployment complexity on Kubernetes.</li>
|
||
</ul>
|
||
<h3 id="flyte">Flyte<a class="headerlink" href="#flyte" title="Permanent link">¶</a></h3>
|
||
<ul>
|
||
<li><strong>(2022)</strong> <a href="https://mlops.community/blog/mlops-with-flyte-the-convergence-of-workflows-between-machine-learning-and-engineering">mlops.community: MLOps with Flyte: The Convergence of Workflows Between Machine Learning and Engineering</a> <span class='md-tag md-tag--warning'>[NONE CONTENT]</span> <span class='md-tag md-tag--critical'>[ADVANCED LEVEL]</span> 🌟🌟🌟 <span class='md-tag md-tag--info'>[COMMUNITY-TOOL]</span> — Deeply discusses how Flyte unifies software engineering workflows with rigorous data science experiments. Emphasizes strict typing, local mock testing, deterministic execution environments, and dynamic workflow generation as essential mechanisms for building resilient production-level AI systems.</li>
|
||
<li><strong>(2022)</strong> <a href="https://mlops.community/blog/flyte-mlops-simplified">mlops.community: MLOps Simplified: orchestrating ML pipelines with infrastructure abstraction. Enabled by Flyte</a> <span class='md-tag md-tag--warning'>[NONE CONTENT]</span> 🌟🌟🌟 <span class='md-tag md-tag--info'>[COMMUNITY-TOOL]</span> — Highlights Flyte's capability to isolate infrastructure concerns from data science domains via clean abstract interfaces. Discusses how Flyte's structural containerized execution guarantees clean, versioned deployments without requiring data scientists to directly configure complex Kubernetes parameters.</li>
|
||
</ul>
|
||
<h3 id="frameworks">Frameworks<a class="headerlink" href="#frameworks" title="Permanent link">¶</a></h3>
|
||
<ul>
|
||
<li><strong>(2024)</strong> <a href="https://www.zenml.io"><strong>zenml.io: ZenML</strong></a> <span class='md-tag md-tag--warning'>[PYTHON CONTENT]</span> 🌟🌟🌟🌟 <span class='md-tag md-tag--success'>[ENTERPRISE-STABLE]</span> — ZenML is an extensible MLOps pipeline framework designed to decouple data engineering and machine learning workflows from physical target infrastructure. It integrates with major cloud stacks and allows reproducible local executions to scale to production environments effortlessly.</li>
|
||
</ul>
|
||
<h3 id="workflows">Workflows<a class="headerlink" href="#workflows" title="Permanent link">¶</a></h3>
|
||
<ul>
|
||
<li><strong>(2024)</strong> <a href="https://github.com/Netflix/metaflow"><mark>github.com/Netflix/metaflow 🌟</mark></a> <span class='md-tag md-tag--info'>⭐ 10128</span> <svg class="v2-sparkline" width="50" height="15" viewBox="0 0 50 15" style="vertical-align: middle; display: inline-block; margin-left: 6px;" title="Activity Trend"><defs><linearGradient id="spark-grad-30434223" x1="0" y1="0" x2="1" y2="0"><stop offset="0%" stop-color="rgba(34, 211, 238, 0.2)" /><stop offset="100%" stop-color="var(--md-accent-fg-color)" /></linearGradient></defs><path class="v2-sparkline-path" d="M 0 5 L 10 8 L 20 10 L 30 13 L 40 13 L 50 5" fill="none" stroke="url(#spark-grad-30434223)" stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round" /><circle cx="50" cy="5" r="2" fill="var(--md-accent-fg-color)" /></svg> <span class='md-tag md-tag--warning'>[PYTHON CONTENT]</span> 🌟🌟🌟🌟🌟 <span class='md-tag md-tag--success'>[DE FACTO STANDARD]</span> — Metaflow is Netflix's human-centric framework designed for building and managing production-grade data science pipelines. It seamlessly integrates local development with enterprise-scale cloud infrastructures, handling data caching, model versioning, and compute scaling automatically.</li>
|
||
</ul>
|
||
<h2 id="software-engineering">Software Engineering<a class="headerlink" href="#software-engineering" title="Permanent link">¶</a></h2>
|
||
<h3 id="devops-1">DevOps (1)<a class="headerlink" href="#devops-1" title="Permanent link">¶</a></h3>
|
||
<h4 id="ai-assisted-workflows">AI-Assisted Workflows<a class="headerlink" href="#ai-assisted-workflows" title="Permanent link">¶</a></h4>
|
||
<ul>
|
||
<li><strong>(2023)</strong> <a href="https://about.gitlab.com/blog/how-is-ai-ml-changing-devops">about.gitlab.com: How is AI/ML changing DevOps?</a> <span class='md-tag md-tag--warning'>[N/A CONTENT]</span> <span class='md-tag md-tag--info'>[COMMUNITY-TOOL]</span> — Investigates the evolving integration of AI and Machine Learning inside DevSecOps pipelines. Discusses automated code suggestion mechanisms, ML-driven vulnerability remediation, and predictive diagnostic models, outlining the performance gains and code-security shifts this creates inside delivery loops.</li>
|
||
</ul>
|
||
<h2 id="workflow-transition">Workflow Transition<a class="headerlink" href="#workflow-transition" title="Permanent link">¶</a></h2>
|
||
<h3 id="best-practices">Best Practices<a class="headerlink" href="#best-practices" title="Permanent link">¶</a></h3>
|
||
<ul>
|
||
<li><strong>(2021)</strong> <a href="https://towardsdatascience.com/from-jupyter-notebooks-to-real-life-mlops-9f590a7b5faa">towardsdatascience.com: From Jupyter Notebooks to Real-life: MLOps 🌟</a> <span class='md-tag md-tag--warning'>[PYTHON CONTENT]</span> 🌟🌟🌟 <span class='md-tag md-tag--info'>[COMMUNITY-TOOL]</span> — Addresses the operational chasm between exploratory research in Jupyter notebooks and reliable, production-grade model deployments. Outlines a structured strategy for code modularization, environmental reproducibility, continuous monitoring, and automated retraining architectures.</li>
|
||
<li><strong>(2021)</strong> <a href="https://www.analyticsvidhya.com/blog/2021/04/bring-devops-to-data-science-with-continuous-mlops">analyticsvidhya.com: Bring DevOps To Data Science With MLOps</a> <span class='md-tag md-tag--warning'>[NONE CONTENT]</span> 🌟🌟🌟 <span class='md-tag md-tag--info'>[COMMUNITY-TOOL]</span> — Examines the application of classic software engineering DevOps disciplines—such as unit testing, infrastructure-as-code, and active monitoring—to machine learning life cycles. Outlines strategies to dismantle structural friction between software engineering teams and research labs.</li>
|
||
</ul>
|
||
<h2 id="workshops">Workshops<a class="headerlink" href="#workshops" title="Permanent link">¶</a></h2>
|
||
<h3 id="infrastructure-1">Infrastructure (1)<a class="headerlink" href="#infrastructure-1" title="Permanent link">¶</a></h3>
|
||
<ul>
|
||
<li><strong>(2022)</strong> <a href="https://github.com/aporia-ai/mlplatform-workshop"><strong>ML Platform Workshop</strong></a> <span class='md-tag md-tag--info'>⭐ 445</span> <svg class="v2-sparkline" width="50" height="15" viewBox="0 0 50 15" style="vertical-align: middle; display: inline-block; margin-left: 6px;" title="Activity Trend"><defs><linearGradient id="spark-grad-8a3327ed" x1="0" y1="0" x2="1" y2="0"><stop offset="0%" stop-color="rgba(34, 211, 238, 0.2)" /><stop offset="100%" stop-color="var(--md-accent-fg-color)" /></linearGradient></defs><path class="v2-sparkline-path" d="M 0 12 L 10 9 L 20 13 L 30 12 L 40 11 L 50 3" fill="none" stroke="url(#spark-grad-8a3327ed)" stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round" /><circle cx="50" cy="3" r="2" fill="var(--md-accent-fg-color)" /></svg> <span class='md-tag md-tag--warning'>[PYTHON CONTENT]</span> 🌟🌟🌟🌟 <span class='md-tag md-tag--success'>[ENTERPRISE-STABLE]</span> — A hands-on technical workshop repository showcasing the design of an end-to-end Machine Learning Platform. Demonstrates real-world integration of model registries, tracking servers, and deployment mechanisms under production-like conditions. Excellent educational resource for learning the architectural glue of modern MLOps frameworks.</li>
|
||
</ul>
|
||
<hr />
|
||
<p>💡 <strong>Explore Related:</strong> <a href="../ai/">AI</a> | <a href="../chatgpt/">ChatGPT</a> | <a href="../ai-agents-mcp/">AI Agents MCP</a></p>
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