Open Source
Awesome Production Machine Learning
A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning
Our verdict
We have not reviewed Awesome Production Machine Learning yet
No one at Enterprise DNA has checked this listing against its sources. The description comes from public listings gathered automatically. Any fact below shows where it came from and when it was last checked.
From the listing
About Awesome Production Machine Learning
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
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
20,585 stars on GitHub. Last updated 2026-06-01. Licensed MIT.
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
Key facts
What we have checked
- Latest version
- release-2026-10-01 (released 1 Oct 2026)
- GitHub stars
- 20,954
Each value shows when we last confirmed it and where it came from. Values we have not confirmed are left out, never guessed.
Stars and usage
How Awesome Production Machine Learning is growing
20,585 to 20,954
Alternatives
Alternatives to Awesome Production Machine Learning
| Record | Type | How we know | Checked |
|---|---|---|---|
| Awesome Open MLOpsAlternative to this | Open source | Named in the listing | Not dated |
| kelvins/awesome-mlopsAlternative to this | Open source | Named in the listing | Not dated |
| visenger/awesome-mlopsAlternative to this | Open source | Named in the listing | Not dated |
Compare
Compare Awesome Production Machine Learning side by side
Sources
Where this comes from
- GitHubOfficial
- github api via nightly crawl, recovered from git history 20e874270887c1008f860ee0198e461a23c9e438Registry
A source shows what was published when we checked it. It does not certify security, performance or a working installation. Read our method.
Changelog
What changed
Released release-2026-10-01
Released release-2026-09-01
Released release-2026-08-01
Released release-2026-07-01
Released release-2026-06-01
Released release-2026-05-01
Released release-2026-03-01
Released release-2026-02-01
Released release-2026-01-01
Released release-2025-12-01
Open-source & AI alternatives
Swap-in tools that solve the same job. Weigh the trade-offs before you commit.
Awesome Open MLOps
Community
The Fuzzy Labs guide to the universe of open source MLOps
kelvins/awesome-mlops
Community
:sunglasses: A curated list of awesome MLOps tools
visenger/awesome-mlops
Community
A curated list of references for MLOps
Pairs with
Other entries in the index that connect to this one. Click through to see the chain.
Argo Workflows
Community
Workflow Engine for Kubernetes
DVC
Community
🦉 Data Versioning and ML Experiments
Kubeflow
Community
Machine Learning Toolkit for Kubernetes
Prefect
Community
Prefect is a workflow orchestration framework for building resilient data pipelines in Python.
Awesome AutoDL
Community
Automated Deep Learning: Neural Architecture Search Is Not the End (a curated list of AutoDL resources and an in-depth analysis)
Awesome AutoML Papers
Community
A curated list of automated machine learning papers, articles, tutorials, slides and projects
Awesome Federated Learning Systems
Community
Federated Learning Systems Paper List
Awesome Tensor Compilers
Community
A list of awesome compiler projects and papers for tensor computation and deep learning.
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