kelvins/awesome-mlops

Overview

A curated GitHub repository listing MLOps tools organized by category. It helps developers discover and compare open-source and commercial tools for machine learning operations, including observability, orchestration, and deployment.

Best for

Best for
Developers and ML teams evaluating or building their MLOps toolchain and seeking a curated starting point

Use cases

• Identifying observability and monitoring tools for ML pipelines
• Comparing MLOps solutions across categories like feature stores, model serving, and data versioning
• Exploring community-vetted tool recommendations with GitHub stars and descriptions

Notes

A curated GitHub repository listing MLOps tools organized by category. It helps developers discover and compare open-source and commercial tools for machine learning operations, including observability, orchestration, and deployment.

5,160 stars on GitHub. Last updated 2026-04-29.

Use cases

• Identifying observability and monitoring tools for ML pipelines
• Comparing MLOps solutions across categories like feature stores, model serving, and data versioning
• Exploring community-vetted tool recommendations with GitHub stars and descriptions

Pros

• Comprehensive overview of the MLOps landscape with 5,160+ stars indicating community trust
• Categorized structure makes it easy to find tools for specific needs like observability or experiment tracking
• Maintained by the community, providing up-to-date entries for widely used tools

Cons

• Not a tool itself; it’s a static list with no interactive features or built-in functionality
• Entries may become outdated if maintainers don’t update regularly
• No quality guarantees or depth; each tool is just a link and brief description

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