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ethicalml/awesome-production-machine-learning

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TLDR

A curated directory of open-source tools for building, deploying, monitoring, and securing machine learning systems in production.

Mindmap

mindmap
  root((repo))
    What it does
      MLOps toolchain
      Production ML systems
      Open-source libraries
    Categories
      Data pipelines
      Model deployment
      Monitoring and evaluation
      Feature stores
    Use cases
      Find ML tools
      Build ML systems
      Learn best practices
    Audience
      ML engineers
      Data scientists
      DevOps teams

Things people build with this

USE CASE 1

Find and compare open-source tools for deploying machine learning models to production.

USE CASE 2

Discover libraries for monitoring model performance, versioning, and scaling ML systems.

USE CASE 3

Learn about data pipelines, feature stores, and experiment tracking tools used by ML teams.

USE CASE 4

Explore tools for model explainability, fairness, privacy, and anomaly detection in production.

Tech stack

PythonMachine LearningMLOpsData Engineering

Getting it running

Difficulty · easy Time to first run · 5min
Use freely for any purpose including commercial, as long as you keep the copyright notice.

In plain English

Awesome Production Machine Learning is a curated list, sometimes called an awesome list, of free libraries that help take machine learning systems from a researcher's laptop into a real production environment. The repository itself is not software you run; it is a long, organized directory of links with one-line descriptions, kept up to date by community contributions. The way it works is that the README is divided into themed sections covering every stage of getting a model into production. Sections include AutoML for automating model and hyperparameter choice, computation and communication optimisation, data annotation and synthesis, data pipelines, data science notebooks, data storage optimisation, data stream processing, deployment and serving, evaluation and monitoring, explainability and fairness, feature stores, anomaly detection, computer vision, information retrieval, natural language processing, recommender systems, reinforcement learning, robotics, visualisation, metadata management, model and experiment management, model storage optimisation, model training and orchestration, and privacy and safety. Each section lists open source projects with short descriptions and links. Someone would use this as a starting point when picking a tool for a specific MLOps job and wanting to skim what is available without doing a fresh search every time. The maintainers publish monthly release notes summarizing newly added libraries, and the README links to a separate search toolkit on Hugging Face Spaces, a related Awesome Production GenAI list, a video introduction, and a Machine Learning Engineer newsletter. The full README is longer than what was provided.

Copy-paste prompts

Prompt 1
I need to set up model monitoring and evaluation for a production ML system. What tools from this awesome list would you recommend?
Prompt 2
Show me the best open-source feature store and model serving options listed in this MLOps directory.
Prompt 3
I'm building a data pipeline for machine learning. Which data processing and annotation tools are recommended in this curated list?
Prompt 4
What are the top-rated tools for experiment tracking and model versioning in production ML according to this repository?
Prompt 5
Help me find privacy-preserving and fairness-focused ML tools from this awesome production ML list.
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