Enterprise DNA
O Open Source Observability medium

Awesome Open MLOps

by Community

The Fuzzy Labs guide to the universe of open source MLOps

AO

OSS

Awesome Open MLOps

Added 1 June 2026

#datascience #devops #infrastructure #machine-learning #machinelearning #mlops

Overview

A community-curated guide to open source MLOps tools, maintained by Fuzzy Labs. It organizes resources by category such as orchestration, monitoring, and feature stores, helping developers discover and compare options.

Best for

Best for
Developers and teams evaluating open source MLOps solutions

Use cases

  • Discovering open source MLOps tools for a new project
  • Comparing alternatives in categories like model serving or experiment tracking
  • Staying updated on the open source MLOps ecosystem

Notes

A community-curated guide to open source MLOps tools, maintained by Fuzzy Labs. It organizes resources by category such as orchestration, monitoring, and feature stores, helping developers discover and compare options.

482 stars on GitHub. Last updated 2025-05-19. Licensed Apache-2.0.

Use cases

  • Discovering open source MLOps tools for a new project
  • Comparing alternatives in categories like model serving or experiment tracking
  • Staying updated on the open source MLOps ecosystem

Pros

  • Comprehensive collection of tools across the MLOps lifecycle
  • Community maintained with regular updates
  • Free and open source resource

Cons

  • Not a tool itself, only a list of links and descriptions
  • Depth of information per tool is limited to a short summary
  • Quality and accuracy depend on community contributions

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

Pros

  • Comprehensive collection of tools across the MLOps lifecycle
  • Community maintained with regular updates
  • Free and open source resource

Cons

  • Not a tool itself, only a list of links and descriptions
  • Depth of information per tool is limited to a short summary
  • Quality and accuracy depend on community contributions