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EthicalML/awesome-production-machine-learning

A curated list of open source libraries for deploying, monitoring, versioning, scaling and securing production machine learning.

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What is it?

Awesome Production Machine Learning is a categorized list of open source tools for taking machine learning models from experimentation into production. Entries are grouped by stage of the workflow, including AutoML, distributed training and computation optimisation, data annotation, data pipelines and orchestration, notebooks, data storage, feature stores, model training and experiment management, deployment and serving, evaluation and monitoring, explainability and privacy.

Each entry links to the project's repository with a short description of what it does. The list also covers domain-specific areas such as computer vision, NLP, recommender systems and reinforcement learning. The maintainers publish monthly summaries of newly added libraries through GitHub releases.

Key features

  • 01Organised into more than 20 categories covering the ML lifecycle from data to serving.
  • 02Each entry links to its repository with a one-line description of its purpose.
  • 03Includes tools for deployment, monitoring, versioning and scaling of models.
  • 04Covers domain-specific libraries for vision, NLP, recommenders and reinforcement learning.
  • 05Monthly release notes summarise newly added libraries.

Use cases

  • Finding a feature store, workflow orchestrator or model serving tool when designing an MLOps stack.
  • Comparing candidate libraries for AutoML or hyperparameter tuning before adopting one.
  • Onboarding onto a team's ML infrastructure by learning which categories of tools exist.
  • Tracking new production ML tools over time through the list's monthly updates.

Topics

#mlops#machine-learning#deep-learning#model-deployment#monitoring#awesome-list#data-pipeline#awesome#data-mining#explainability

FAQ

What is awesome-production-machine-learning?
A curated list of open source libraries for deploying, monitoring, versioning, scaling and securing production machine learning. Awesome Production Machine Learning is a categorized list of open source tools for taking machine learning models from experimentation into production. Entries are grouped by stage of the workflow, including AutoML, distributed training and computation optimisation, data annotation, data pipelines and orchestration, notebooks, data storage, feature stores, model training and experiment management, deployment and serving, evaluation and monitoring, explainability and privacy.
Is awesome-production-machine-learning free to use? What license does it have?
awesome-production-machine-learning is open source under the MIT license. Check the license terms before using it in commercial products.
Is awesome-production-machine-learning still maintained?
Actively maintained. The last commit to awesome-production-machine-learning was on Oct 18, 2026.
What can I use awesome-production-machine-learning for?
Finding a feature store, workflow orchestrator or model serving tool when designing an MLOps stack. Comparing candidate libraries for AutoML or hyperparameter tuning before adopting one. Onboarding onto a team's ML infrastructure by learning which categories of tools exist. Tracking new production ML tools over time through the list's monthly updates.
Where can I get awesome-production-machine-learning?
The source code and installation instructions are on GitHub: https://github.com/EthicalML/awesome-production-machine-learning

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