visenger/awesome-mlops

Overview

A curated GitHub repository collecting references, tools, and best practices for MLOps workflows. Covers monitoring, deployment, versioning, and operational patterns for machine learning systems in production.

Best for

Best for
Teams building or evaluating MLOps infrastructure who need a structured starting point for tool discovery

Use cases

• Finding MLOps tools and frameworks for your stack
• Learning MLOps patterns and architectural approaches
• Discovering monitoring and observability solutions for ML models

Notes

A curated GitHub repository collecting references, tools, and best practices for MLOps workflows. Covers monitoring, deployment, versioning, and operational patterns for machine learning systems in production.

13,923 stars on GitHub. Last updated 2024-11-21.

Use cases

• Finding MLOps tools and frameworks for your stack
• Learning MLOps patterns and architectural approaches
• Discovering monitoring and observability solutions for ML models

Pros

• Community-maintained with 13k+ stars, indicating broad adoption and relevance
• Organized reference list reduces research time for MLOps decisions
• Covers the full MLOps lifecycle from training to monitoring

Cons

• A curated list, not a tool itself. Requires manual evaluation of each referenced project
• No hands-on guidance or integration examples. Links to external resources without implementation details
• Maintenance depends on community contributions. Coverage may lag emerging tools

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