Awesome Production Machine Learning
by Community
A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning
OSS
Awesome Production Machine Learning
Added 1 June 2026
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.
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
Pairs with
Other entries in the index that connect to this one. Click through to see the chain.
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