Awesome Production Machine Learning

Overview

A curated GitHub repository listing open source libraries for deploying, monitoring, versioning, and scaling machine learning models in production. Covers the full ML ops lifecycle from model serving to observability. Community-maintained with 20k+ stars.

Best for

Best for
Teams building ML infrastructure who need a starting point for evaluating open source ops tools

Use cases

• Finding vetted open source tools for ML model deployment
• Discovering monitoring and versioning solutions for production ML systems
• Evaluating scaling strategies and infrastructure options

Notes

A curated GitHub repository listing open source libraries for deploying, monitoring, versioning, and scaling machine learning models in production. Covers the full ML ops lifecycle from model serving to observability. Community-maintained with 20k+ stars.

20,585 stars on GitHub. Last updated 2026-06-01. Licensed MIT.

Use cases

• Finding vetted open source tools for ML model deployment
• Discovering monitoring and versioning solutions for production ML systems
• Evaluating scaling strategies and infrastructure options

Pros

• Comprehensive coverage of the ML ops stack in one place
• Community-curated with high visibility (20k+ stars)
• Links directly to actual libraries rather than abstractions

Cons

• A list, not a tool. Requires manual evaluation and integration of each library
• No hands-on guidance on which combinations work well together
• Maintenance quality depends on community contributions

Indexed from awesome-llmops and enriched against its public facts.