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39 lines
5.4 KiB
Markdown
39 lines
5.4 KiB
Markdown
# Machine Learning Ops (MLOps)
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- [Introduction](#introduction)
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- [Kubeflow](#kubeflow)
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- [Tweets](#tweets)
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## Introduction
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- [cd.foundation: Announcing the CD Foundation MLOps SIG](https://cd.foundation/blog/2020/02/11/announcing-the-cd-foundation-mlops-sig/)
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- [dafriedman97.github.io: Machine Learning from Scratch](https://dafriedman97.github.io/mlbook/content/introduction.html) Derivations in Concept and Code.
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- [cortex.dev: How to build a pipeline to retrain and deploy models](https://www.cortex.dev/post/how-to-build-a-pipeline-to-retrain-and-deploy-models)
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- [github: A very Long never ending Learning around Data Engineering & Machine Learning](https://github.com/abhishek-ch/around-dataengineering)
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- [towardsdatascience.com: A Kubernetes architecture for machine learning web-application deployments](https://towardsdatascience.com/a-kubernetes-architecture-for-machine-learning-web-application-deployments-632f7765ef29) Use Kubernetes to reduce machine learning infrastructure costs and scale resources with ease.
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- [cloud.google.com: How to use a machine learning model from a Google Sheet using BigQuery ML](https://cloud.google.com/blog/topics/developers-practitioners/how-use-machine-learning-model-google-sheet-using-bigquery-ml)
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- [itnext.io: Building ML Componentes on Kubernetes](https://itnext.io/building-ml-componentes-on-kubernetes-fc7e24cb9269)
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- [towardsdatascience.com: Deploying An ML Model With FastAPI — A Succinct Guide](https://towardsdatascience.com/deploying-an-ml-model-with-fastapi-a-succinct-guide-69eceda27b21)
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- [analyticsvidhya.com: Bring DevOps To Data Science With MLOps](https://www.analyticsvidhya.com/blog/2021/04/bring-devops-to-data-science-with-continuous-mlops/)
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- [redhat.com: Introducing Red Hat OpenShift Data Science](https://www.redhat.com/en/blog/introducing-red-hat-openshift-data-science)
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- [towardsdatascience.com: Production Machine Learning Monitoring: Outliers, Drift, Explainers & Statistical Performance](https://towardsdatascience.com/production-machine-learning-monitoring-outliers-drift-explainers-statistical-performance-d9b1d02ac158) 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.
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- [towardsdatascience.com: From DevOps to MLOPS: Integrate Machine Learning Models using Jenkins and Docker](https://towardsdatascience.com/from-devops-to-mlops-integrate-machine-learning-models-using-jenkins-and-docker-79034dbedf1) How to automate data science code with Jenkins and Docker: MLOps = ML + DEV + OPS
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- [cloudblogs.microsoft.com: Simple steps to create scalable processes to deploy ML models as microservices](https://cloudblogs.microsoft.com/opensource/2021/07/09/simple-steps-to-create-scalable-processes-to-deploy-ml-models-as-microservices/)
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- [ML Platform Workshop](https://github.com/aporia-ai/mlplatform-workshop) Example code for a basic ML Platform based on Pulumi, FastAPI, DVC, MLFlow and more
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- [rubrix](https://github.com/recognai/rubrix) A free and open-source tool to explore, label, and monitor data for NLP projects.
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- [towardsdatascience.com: Automatically Generate Machine Learning Code with Just a Few Clicks](https://towardsdatascience.com/automatically-generate-machine-learning-code-with-just-a-few-clicks-7901b2334f97) Using Traingenerator to easily create PyTorch and scikit-learn template codes for machine learning model training
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- [towardsdatascience.com: Schemafull streaming data processing in ML pipelines](https://towardsdatascience.com/using-kafka-with-avro-in-python-da85b3e0f966) Making containerized Python streaming data pipelines leverage schemas for data validation using Kafka with AVRO and Schema Registry
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- [analyticsindiamag.com: Top tools for enabling CI/CD in ML pipelines](https://analyticsindiamag.com/top-tools-for-enabling-ci-cd-in-ml-pipelines/)
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## Kubeflow
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- [kubeflow](https://www.kubeflow.org/) The Machine Learning Toolkit for Kubernetes
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- [medium.com: Machine Learning using Kubeflow](https://medium.com/cloud-techies/machine-learning-using-kubeflow-ad7c9f767df0)
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- [infracloud.io: Machine Learning Orchestration on Kubernetes using Kubeflow](https://www.infracloud.io/blogs/machine-learning-orchestration-kubernetes-kubeflow/)
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## Tweets
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<details>
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<summary>Click to expand!</summary>
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<center>
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<blockquote class="twitter-tweet"><p lang="en" dir="ltr">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. 🧵👇</p>— Maria Khalusova (@mariaKhalusova) <a href="https://twitter.com/mariaKhalusova/status/1331982686819389440?ref_src=twsrc%5Etfw">November 26, 2020</a></blockquote> <script async src="https://platform.twitter.com/widgets.js" charset="utf-8"></script>
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<blockquote class="twitter-tweet"><p lang="en" dir="ltr">You don't need to go to a university to learn machine learning - you can do it from your living room, for completely free.<br><br>Here is an extensive list of curated free courses and tutorials, from beginner to advanced. ↓<br><br>(Trust me, you want to bookmark this tweet.)</p>— Tivadar Danka (@TivadarDanka) <a href="https://twitter.com/TivadarDanka/status/1440281314398138373?ref_src=twsrc%5Etfw">September 21, 2021</a></blockquote> <script async src="https://platform.twitter.com/widgets.js" charset="utf-8"></script>
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</center>
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</details> |