EthicalML/awesome-production-machine-learning
A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning
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README & documentation
Source previewRead the project’s overview, installation instructions and usage examples. The original README is the source of truth.
[](https://awesome.re) [](https://twitter.com/EthicalML) This repository contains a curated list of awesome open source libraries that will help you deploy, monitor, version, scale, and secure your production machine learning 🚀 You can keep up to date by watching this github repo to get a summary of the new production ML libraries added every month via releases 🤩 Additionally, we provide a…
Read the full README ↗ · Preview checked 2026-10-27T15:18:27.468Z
Inside the original README — Document outline
- Awesome Production Machine Learning
- Quick links to sections on this page
- Contributing to the list
- 10 Min Video Overview
- Want to receive recurrent updates on this repo and other advancements?
- Main Content
- AutoML
- Computation and Communication Optimisation
- Data Annotation and Synthesis
- Data Pipeline
- Data Science Notebook
- Data Storage Optimisation
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What this repository does
A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning
Repository facts
- Owner
- EthicalML
- Primary language
- Not reported
- Stars
- 21,063
- Forks
- 2,616
- Open issues + pull requests
- 43
- License
- MIT
- Archived
- No
- Default branch
- master
- Created
- 2018-08-15T14:28:41.000Z
- Last push
- 2026-11-01T05:14:48.000Z
Topics and intended use
Owner-supplied topics: awesome, awesome-list, data-mining, deep-learning, explainability, interpretability, large-scale-machine-learning, large-scale-ml, machine-learning, machine-learning-operations, ml-operations, ml-ops, mlops, privacy-preserving, privacy-preserving-machine-learning, privacy-preserving-ml, production-machine-learning, production-ml, responsible-ai
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Source and freshness
Source: GitHub. Metadata observed 2026-11-01T12:19:42.808Z. Daily imports are snapshots, not real-time monitoring.
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