Guide · Open source
How to use awesome-production-machine-learning: a guide to the list
Updated · written with AI from the project's own documentation and checked against our catalogue data
awesome-production-machine-learning is a curated, categorized list of open source libraries that help teams take machine learning models from experiments into real production use. It is a reading resource, not a program you install. This guide shows you how to navigate the list, find the category you need, and decide which linked projects are worth a closer look.
awesome-production-machine-learningEthicalML/awesome-production-machine-learning · ★ 21.1k · MIT Install options & downloads Quick start
- 1Open the list. Go to the GitHub repository or the project homepage to read the README online.
- 2Pick a category. Use the quick-links table to jump to the area that matches your problem, such as deployment or monitoring.
- 3Shortlist projects. Note two or three entries that solve the same problem and open their project pages.
- 4Check each project. Review licence, documentation and activity on the linked project page, since the list does not test or rank them.
- 5Optionally clone locally. Run git clone on the repository if you want an offline copy of the list.
- 6Stay updated. Watch the repository and follow its releases for monthly summaries of new additions.
What is awesome-production-machine-learning?
The repository is a list maintained by EthicalML. Its README describes it as a curated list of open source libraries that help you deploy, monitor, version, scale, and secure production machine learning. Each entry names a project and links to its page, with a short description of what it does.
The list is released under the MIT licence. Nothing in it runs on your computer by itself, so there is no software to set up beyond what you choose to use from the linked projects.
Find the right category on the list
The README opens with a table of quick links to sections. Categories cover areas such as AutoML, Data Pipeline, Deployment & Serving, Evaluation & Monitoring, Feature Store, Model Training & Orchestration, Model, Data & Experiment Management, and Privacy & Safety. Other sections cover data annotation, data stream processing, explainability and fairness, and several industry-strength domains such as computer vision and natural language processing.
Start from the problem you have. If you need to track model versions or experiments, go to the management section. If you are choosing how to serve predictions, go to deployment and serving.
- Click any entry in the quick-links table to jump to that section.
- Use the search toolkit linked in the README to move through the toolchain more quickly.
Read and compare entries
Each entry has a link to the project, an icon for its GitHub star count, and a one-line description. The description is the README's own summary, so read the project page before deciding anything. Entries assume you already know terms such as feature stores and hyperparameter tuning.
The README does not rank, test or recommend the listed projects. Treat the list as a shortlist to research, and check each project's licence, activity and documentation yourself.
- Open the linked project page to see its licence and how it is maintained.
- Compare two or three tools that solve the same problem before choosing one.
- Keep a short list of candidates with notes on the features you need.
Keep up with new additions
The README says the maintainers publish a summary of newly added production ML libraries each month through the repository's releases page. Watching the repository on GitHub is the way to receive these updates.
The README also points to a newsletter for the Machine Learning Engineer community and to a companion list, Awesome Production GenAI, which covers generative AI applications and systems.
- Watch the GitHub repository and choose releases to get monthly summaries.
- Check the 10-minute video overview linked in the README for a high-level tour of the tools.
Optionally clone the repository locally
Cloning is optional. It gives you a local copy of the README and list so you can search it with your own tools. You do not need to clone it to read the list online.
git clone https://github.com/EthicalML/awesome-production-machine-learningWhat people use it for
- An ML engineer scanning the deployment and serving section to shortlist tools for model serving.
- A data team comparing options in the model, data and experiment management section for versioning and experiment tracking.
- An engineering lead using the list to get a broad overview of the production ML stack before choosing components.
- A student mapping which areas of production ML exist, such as AutoML, monitoring and privacy and safety.
Frequently asked questions
Is awesome-production-machine-learning free to use?
The repository is released under the MIT licence, as stated in its listing. The linked projects each have their own licences, so check the licence on each project page before using it.
Do I need to install awesome-production-machine-learning?
No. It is a list you read online. The README's only command is an optional git clone to make a local copy of the repository.
What are the requirements for using this list?
The source lists no prerequisites. To read it, you only need a web browser. Individual tools you choose from the list will have their own requirements.
Is awesome-production-machine-learning a good starting point for beginners?
It is best for readers with some machine learning background, because entries use terms such as feature stores and hyperparameter tuning without explaining them.
How does it compare to awesome-mlops?
Both are curated lists of tools for operating machine learning. This list focuses on production ML libraries and includes sections such as privacy and safety, while awesome-mlops is a separate curated list. Compare their categories to see which fits your needs.
Does the list say which tools are best?
No. The README does not rank or test the listed projects, so it works as a shortlist to research rather than a verified recommendation.
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Our verdict
This list suits ML engineers, data teams and engineering leads who want a broad, categorized overview of open source production ML tools. It is not a single application, and it is not suited to beginners or anyone who needs tested recommendations; in those cases, pick a ready-to-run tool or a guide written for newcomers.
Install options & downloads