awesome-production-machine-learning open source analysis

A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning

Project overview

⭐ 19894 · Last activity on GitHub: 2026-01-04

GitHub: https://github.com/EthicalML/awesome-production-machine-learning

Why it matters for engineering teams

Awesome-production-machine-learning addresses the complex challenges of deploying, monitoring, versioning and scaling machine learning models in production environments. It offers a curated collection of open source tools that help engineering teams streamline ML operations, ensuring models remain reliable and maintainable over time. This repository is particularly suited for machine learning and AI engineering teams who require a production ready solution that integrates with existing workflows and supports responsible AI practices. The maturity of the listed projects varies, but many are widely adopted in industry, making it a practical resource for real-world applications. However, it may not be the right choice for teams seeking a single turnkey platform or those new to ML operations, as it requires some expertise to select and integrate the appropriate components effectively.

When to use this project

This repository is a strong choice when your team needs a comprehensive open source tool for engineering teams to manage the full lifecycle of production machine learning models. Consider alternatives if you require a fully managed service or have limited resources for maintaining self hosted options.

Team fit and typical use cases

Machine learning engineers and AI specialists benefit most from this repository, using it to deploy and monitor models in production environments. Data engineers and DevOps professionals also find value in integrating these tools to support scalable ML pipelines. It is commonly used in products involving large-scale machine learning, privacy-preserving techniques and responsible AI implementations.

Best suited for

Topics and ecosystem

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

Activity and freshness

Latest commit on GitHub: 2026-01-04. Activity data is based on repeated RepoPi snapshots of the GitHub repository. It gives a quick, factual view of how alive the project is.