awesome-production-machine-learning
A curated list of open source libraries for deploying, monitoring, versioning and scaling machine learning in
- Stars
- 21.1k
- Forks
- 2.6k
- Licence
- MIT
- Language
- —
- Last update
- 5 days ago
- Type
- Curated list
What it is
This is a categorized list of open source tools that help teams take machine learning models from experiments into real production use. It covers areas such as AutoML, deployment and serving, monitoring, feature stores, and privacy and safety, with each entry linking to its project page. People use it as a starting point for finding and comparing tools rather than as software to install.
- Browse the Deployment & Serving or Evaluation & Monitoring sections to shortlist tools for a project
- Compare AutoML libraries such as AutoGluon, FLAML and Optuna for a tabular modeling task
- Find privacy and explainability libraries when adding fairness or safety checks to a model
- Follow monthly release notes to see newly added production ML libraries
Great for
- ML engineers looking for tools to deploy or monitor models
- Data teams scouting options for model versioning, experiment tracking or feature stores
- Engineering leads building a production ML stack who want a broad overview
- Researchers or students wanting to see what the production ML ecosystem includes
Not ideal for
- People who want a single ready-to-run application, since this is only a list of other projects
- Beginners without ML background, because the entries assume familiarity with terms like feature stores and hyperparameter tuning
- Anyone needing a verified recommendation, since the README does not rank or test the listed projects
How to get it
- 1
Read it online
Clone the repository locally (optional)
git clone https://github.com/EthicalML/awesome-production-machine-learning - 2
Run it
- Open the repository or the homepage linked in the project metadata
- Use the quick-links table in the README to jump to a section relevant to your needs
- Follow the links to individual projects and check their own documentation
- Watch the repository releases to receive monthly summaries of new additions
- 3
Get the most out of it
- Use the search toolkit linked in the README to navigate the toolchain more quickly
- Check the release notes to track newly added libraries over time
- Read the linked video overviews for background on MLOps motivations and tools
- Review CONTRIBUTING.md if you want to suggest additions
Downloads
Strengths
- Broad coverage across the full production ML lifecycle in one organized place
- Sections are grouped by function, with a quick-links table for navigation
- Maintained with monthly release summaries and a contribution process
Trade-offs
- It is a list, so each tool still needs separate evaluation for fit, maturity and maintenance
- The README excerpt provided is truncated, so not every section could be reviewed
How it compares
| Project | Stars | Type | Level | Updated | Best for |
|---|---|---|---|---|---|
| awesome-production-machine-learning (this) | 21.1k | Curated list | beginner | 5 days ago | ML engineers looking for tools to deploy or monitor models |
| shap | 25.8k | Library | intermediate | 4 days ago | Data scientists who need to explain tree-based models such as XGBoost, LightGBM, or CatBoost |
| Made-With-ML | 49.9k | Learning resource | intermediate | 8 months ago | Software engineers who want to learn how ML models get deployed and maintained |
| wandb | 11.3k | Library | beginner | 4 days ago | Machine learning engineers who want to compare training runs over time |
| netron | 33.6k | App | beginner | 4 days ago | Engineers who need to check a model's layers and tensor connections before deploying it |
| applied-ml | 30.8k | Curated list | beginner | 2.3 years ago | Engineers planning an ML project who want to see how other companies approached a similar problem |
Stats synced from GitHub. The assessment above was drafted with AI from the project's README on 2026-10-29 and may miss recent changes — check the README before relying on a command. MIT licence: free to use, modify and distribute, including commercially, as long as you keep the copyright notice.