awesome-kubernetes/docs/mlops.md

Machine Learning Ops (MLOps) and Data Science

• Introduction. MLOps
• Kubeflow
• KServe Cloud Native Model Server
• Data Science
• Tweets

Introduction. MLOps

• cd.foundation: Announcing the CD Foundation MLOps SIG
• dafriedman97.github.io: Machine Learning from Scratch Derivations in Concept and Code.
• cortex.dev: How to build a pipeline to retrain and deploy models
• github: A very Long never ending Learning around Data Engineering & Machine Learning
• towardsdatascience.com: A Kubernetes architecture for machine learning web-application deployments Use Kubernetes to reduce machine learning infrastructure costs and scale resources with ease.
• cloud.google.com: How to use a machine learning model from a Google Sheet using BigQuery ML
• itnext.io: Building ML Componentes on Kubernetes
• towardsdatascience.com: Deploying An ML Model With FastAPI — A Succinct Guide
• towardsdatascience.com: Production Machine Learning Monitoring: Outliers, Drift, Explainers & Statistical Performance A practical deep dive on production monitoring architectures for machine learning at scale using real-time metrics, outlier detectors, drift detectors, metrics servers and explainers.
• cloudblogs.microsoft.com: Simple steps to create scalable processes to deploy ML models as microservices
• ML Platform Workshop Example code for a basic ML Platform based on Pulumi, FastAPI, DVC, MLFlow and more
• rubrix A free and open-source tool to explore, label, and monitor data for NLP projects.
• towardsdatascience.com: Automatically Generate Machine Learning Code with Just a Few Clicks Using Traingenerator to easily create PyTorch and scikit-learn template codes for machine learning model training
• towardsdatascience.com: Schemafull streaming data processing in ML pipelines Making containerized Python streaming data pipelines leverage schemas for data validation using Kafka with AVRO and Schema Registry
• analyticsindiamag.com: Top tools for enabling CI/CD in ML pipelines
• towardsdatascience.com: Step-by-step Approach to Build Your Machine Learning API Using Fast API A fast and simple approach to serve your model as an API
• ravirajag.dev: MLOps Basics - Week 10: Summary

Kubeflow

• kubeflow The Machine Learning Toolkit for Kubernetes
• medium.com: Machine Learning using Kubeflow
• infracloud.io: Machine Learning Orchestration on Kubernetes using Kubeflow
• blog.devgenius.io: Kubeflow Cloud Deployment (AWS) How do you deploy Kubeflow on AWS? Kubeflow is resource-intensive and deploying it locally means that you might not have enough resources to run your end-to-end machine learning pipeline. In this article you will learn how to deploy Kubeflow in AWS.

KServe Cloud Native Model Server

• kserve.github.io Highly scalable and standards based Model Inference Platform on Kubernetes for Trusted AI
• thenewstack.io: KServe: A Robust and Extensible Cloud Native Model Server

Data Science

• analyticsvidhya.com: Bring DevOps To Data Science With MLOps
• analyticsindiamag.com: Is coding necessary to work as a data scientist? Non-programmers with a no-coding background can have a glorious career in data science and programming, and coding knowledge is more like a skill and not a criterion.
• redhat.com: Introducing Red Hat OpenShift Data Science
• towardsdatascience.com: From DevOps to MLOPS: Integrate Machine Learning Models using Jenkins and Docker How to automate data science code with Jenkins and Docker: MLOps = ML + DEV + OPS
• towardsdatascience.com: How to Structure a Data Science Project for Readability and Transparency And How to Create One in One Line of Code

Tweets

Click to expand!

To my JVM friends looking to explore Machine Learning techniques - you don’t necessarily have to learn Python to do that. There are libraries you can use from the comfort of your JVM environment. 🧵👇

— Maria Khalusova (@mariaKhalusova) November 26, 2020

<script async src="https://platform.twitter.com/widgets.js" charset="utf-8"></script>

You don't need to go to a university to learn machine learning - you can do it from your living room, for completely free.

Here is an extensive list of curated free courses and tutorials, from beginner to advanced. ↓

(Trust me, you want to bookmark this tweet.)

— Tivadar Danka (@TivadarDanka) September 21, 2021

<script async src="https://platform.twitter.com/widgets.js" charset="utf-8"></script>

I started taking data science courses last year, after studying and coding for at least 10 hours 6 days a week and doing several ML projects alongside data analysis projects, I finally got my first data analyst offer from a Nigerian bank last week after countless rejections

— Sam (@SamsonTontoye) February 20, 2022

<script async src="https://platform.twitter.com/widgets.js" charset="utf-8"></script>