Comparison
awesome-production-machine-learning vs awesome-mlops
Verdict
Pick awesome-production-machine-learning when tags unique to awesome-production-machine-learning: inference-serving, machine-learning-operations, ml-ops, model-deployment; pick awesome-mlops when tags unique to awesome-mlops: ai, awesome, data-science, machine-learning.
Markdown twin · awesome-production-machine-learning alternatives · awesome-mlops alternatives
GraphCanon updated 3w
awesome-production-machine-learning
EthicalML/awesome-production-machine-learning
★ 21kpushed Aug 1, 2026
Trust & integrity
| Signal | awesome-production-machine-learning | awesome-mlops |
|---|---|---|
| Maintenance | Very active (3d since push) As of 3w · github_public_v1 | Slowing (97d since push) As of 3w · github_public_v1 |
| Provenance | Not a fork · Organization account As of 3w · github_public_v1 | Not a fork · Personal account As of 3w · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of 1mo · osv@v1 | No lockfile (source not queried) As of 1mo · osv@v1 |
| deps.dev advisories | Not queried deps.dev@v1 | Not queried deps.dev@v1 |
| OpenSSF Scorecard | Not queried openssf-scorecard@v1 | Not queried openssf-scorecard@v1 |
Tagline
- awesome-production-machine-learning
- A curated list of awesome open source libraries for deploying, monitoring, versioning and scaling machine learning
- awesome-mlops
- A curated list of awesome MLOps tools.
Stars
- awesome-production-machine-learning
- 21k
- awesome-mlops
- 5.2k
Forks
- awesome-production-machine-learning
- 2.6k
- awesome-mlops
- 762
Open issues
- awesome-production-machine-learning
- 31
- awesome-mlops
- 71
Language
- awesome-production-machine-learning
- -
- awesome-mlops
- Python
Adopt for
- awesome-production-machine-learning
- -
- awesome-mlops
- Awesome MLOps is a curated list of tools encompassing AutoML to CI/CD for ML.
Persona
- awesome-production-machine-learning
- -
- awesome-mlops
- -
Runtime
- awesome-production-machine-learning
- -
- awesome-mlops
- -
License
- awesome-production-machine-learning
- MIT license making it free for use in both personal and commercial projects without requiring royalty payment or source-code disclosure.
- awesome-mlops
- -
Last pushed
- awesome-production-machine-learning
- Aug 1, 2026
- awesome-mlops
- Apr 29, 2026
Categories
- awesome-production-machine-learning
- Data & Retrieval, Evaluation & Observability, Inference & Serving
- awesome-mlops
- Developer Tools, Evaluation & Observability, Inference & Serving, Model Training
Trust and health
Maintenance
- awesome-production-machine-learning
- Very active (96%)
- awesome-mlops
- Slowing (36%)
Days since push
- awesome-production-machine-learning
- 3d
- awesome-mlops
- 97d
Open issues (now)
- awesome-production-machine-learning
- 31
- awesome-mlops
- 71
Owner type
- awesome-production-machine-learning
- Organization
- awesome-mlops
- User
Full report
- awesome-production-machine-learning
- Trust report
- awesome-mlops
- Trust report
Shared compatibility
- Python · awesome-production-machine-learning: Python runtime · awesome-mlops: Python runtime
Choose awesome-production-machine-learning if…
- Tags unique to awesome-production-machine-learning: inference-serving, machine-learning-operations, ml-ops, model-deployment.
- Also covers Data & Retrieval.
- If you need a diverse set of open-source tools for end-to-end production machine learning tasks
When NOT to use awesome-production-machine-learning
- If you seek a comprehensive solution integrated into one platform rather than selecting from diverse tools
- When your project is specific to only one aspect of machine learning like just deployment or monitoring, and not for the entire workflow
- For teams preferring vendor-specific solutions over open-source options
Choose awesome-mlops if…
- Tags unique to awesome-mlops: ai, awesome, data-science, machine-learning.
- Also covers Developer Tools, Model Training.
- You need resources across multiple facets of the machine-learning pipeline, from data validation to model serving.
When NOT to use awesome-mlops
- In search of a single comprehensive tool for end-to-end ML project management; Awesome MLOps is a repository of links rather than a standalone platform.
- Looking for proprietary solutions or detailed vendor-specific documentation as it focuses on broad, open-source offerings.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (EthicalML/awesome-production-machine-learning) · observed Aug 4, 2026
- GitHub forks (EthicalML/awesome-production-machine-learning) · observed Aug 4, 2026
- Last push (EthicalML/awesome-production-machine-learning) · observed Aug 1, 2026
- License file (MIT) · observed Aug 4, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (kelvins/awesome-mlops) · observed Aug 4, 2026
- GitHub forks (kelvins/awesome-mlops) · observed Aug 4, 2026
- Last push (kelvins/awesome-mlops) · observed Apr 29, 2026
- License file (unknown) · observed Aug 4, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: awesome-production-machine-learning 21k · awesome-mlops 5.2k (synced Aug 4, 2026).
Common questions
- What is the difference between awesome-production-machine-learning and awesome-mlops?
- awesome-production-machine-learning: A curated list of awesome open source libraries for deploying, monitoring, versioning and scaling machine learning. awesome-mlops: A curated list of awesome MLOps tools.. See the comparison table for live GitHub stats and shared categories.
- When should I choose awesome-production-machine-learning over awesome-mlops?
- Choose awesome-production-machine-learning over awesome-mlops when Tags unique to awesome-production-machine-learning: inference-serving, machine-learning-operations, ml-ops, model-deployment; Also covers Data & Retrieval; If you need a diverse set of open-source tools for end-to-end production machine learning tasks.
- When should I choose awesome-mlops over awesome-production-machine-learning?
- Choose awesome-mlops over awesome-production-machine-learning when Tags unique to awesome-mlops: ai, awesome, data-science, machine-learning; Also covers Developer Tools, Model Training; You need resources across multiple facets of the machine-learning pipeline, from data validation to model serving.
- When should I avoid awesome-production-machine-learning?
- If you seek a comprehensive solution integrated into one platform rather than selecting from diverse tools When your project is specific to only one aspect of machine learning like just deployment or monitoring, and not for the entire workflow For teams preferring vendor-specific solutions over open-source options
- When should I avoid awesome-mlops?
- In search of a single comprehensive tool for end-to-end ML project management; Awesome MLOps is a repository of links rather than a standalone platform. Looking for proprietary solutions or detailed vendor-specific documentation as it focuses on broad, open-source offerings.
- Is awesome-production-machine-learning or awesome-mlops more popular on GitHub?
- awesome-production-machine-learning has more GitHub stars (20,821 vs 5,229). Stars measure visibility, not whether either tool fits your constraints.
- Are awesome-production-machine-learning and awesome-mlops open source?
- Yes - both are open-source projects on GitHub.
- Where can I find alternatives to awesome-production-machine-learning or awesome-mlops?
- GraphCanon lists graph-backed alternatives at awesome-production-machine-learning alternatives and awesome-mlops alternatives (awesome-production-machine-learning markdown twin, awesome-mlops markdown twin), ranked by typed relationship edges rather than popularity votes.
- Is there a machine-readable version of this comparison?
- Yes. The markdown twin at this comparison mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.
- Which is better maintained, awesome-production-machine-learning or awesome-mlops?
- awesome-production-machine-learning: Very active. awesome-mlops: Slowing. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance.
- Where are the full trust reports for awesome-production-machine-learning and awesome-mlops?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: awesome-production-machine-learning trust report; awesome-mlops trust report.