Comparison
clearml vs ml-engineering
Verdict
Pick clearml if clearML is an MLOps LLMOps solution that streamlines AI workloads through comprehensive experiment management, data handling, pipeline orchestration, and model serving under one platform; pick ml-engineering if ml-engineering provides an extensive coverage on topics like debugging, GPU utilization, PyTorch, scalability techniques including SLURM setup - essential for those deep-diving into machine learning engineering aspects.
Markdown twin · clearml alternatives · ml-engineering alternatives
GraphCanon updated 4d
vs
Trust & integrity
| Signal | clearml | ml-engineering |
|---|---|---|
| Maintenance | Active (7d since push) As of 2w · github_public_v1 | Very active (2d since push) As of 4d · github_public_v1 |
| Provenance | Not a fork · Organization account As of 2w · github_public_v1 | Not a fork · Personal account As of 4d · github_public_v1 |
| OSV dependency advisories | Published findings 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
- clearml
- MLOps/LLMOps solution for CI/CD in AI workloads
- ml-engineering
- Machine Learning Engineering Open Book
Stars
- clearml
- 6.8k
- ml-engineering
- 19k
Forks
- clearml
- 785
- ml-engineering
- 1.2k
Open issues
- clearml
- 573
- ml-engineering
- 3
Language
- clearml
- Python
- ml-engineering
- Python
Adopt for
- clearml
- ClearML is an MLOps LLMOps solution that streamlines AI workloads through comprehensive experiment management, data handling, pipeline orchestration, and model serving under one platform.
- ml-engineering
- ml-engineering provides an extensive coverage on topics like debugging, GPU utilization, PyTorch, scalability techniques including SLURM setup - essential for those deep-diving into machine learning engineering aspects.
Persona
- clearml
- -
- ml-engineering
- -
Runtime
- clearml
- -
- ml-engineering
- -
License
- clearml
- Apache-2.0
- ml-engineering
- CC-BY-SA-4.0
Last pushed
- clearml
- Jul 27, 2026
- ml-engineering
- Aug 14, 2026
Categories
- clearml
- Inference & Serving, Model Training
- ml-engineering
- Developer Tools, Inference & Serving, Model Training
Trust and health
Maintenance
- clearml
- Active (82%)
- ml-engineering
- Very active (96%)
Days since push
- clearml
- 7d
- ml-engineering
- 2d
Open issues (now)
- clearml
- 573
- ml-engineering
- 3
Stars delta
- clearml
- Unknown
- ml-engineering
- +216 (30d)
Open issues delta
- clearml
- Unknown
- ml-engineering
- +1 (30d)
Owner type
- clearml
- Organization
- ml-engineering
- User
OSV dependency advisories
- clearml
- Published findings
- ml-engineering
- No lockfile (source not queried)
Full report
- clearml
- Trust report
- ml-engineering
- Trust report
Choose clearml if…
- License: clearml is Apache-2.0, ml-engineering is CC-BY-SA-4.0.
- Tags unique to clearml: clearml, control, deep-learning, deeplearning.
- When you require a single platform for managing experiments, orchestrating pipelines, and serving models in your AI projects
When NOT to use clearml
- Avoid if you need deep support for languages other than Python since ClearML is primarily built around Python
- Consider alternatives if your MLOps needs do not include a centralized orchestration platform, as ClearML emphasizes integrated solutions
Choose ml-engineering if…
- License: ml-engineering is CC-BY-SA-4.0, clearml is Apache-2.0.
- Requirements: This resource is a documentation repository and does not have specific system requirements typical of software installations. Reading assumes availability of a僚.
- Tags unique to ml-engineering: debugging, gpus, inference, large language models.
- Also covers Developer Tools.
- - **Extensive Learning Resource**: If you are looking for a detailed read that covers a broad array of ML engineering practices and principles.
When NOT to use ml-engineering
- - **Immediate Hands-On Code Snippets**: If you prefer a repository that provides specific code samples or tutorials rather than explanatory text.
- - **Vendor-Specific Tools Focus**: For users primarily focusing on tools from proprietary vendors where detailed, technical book content might not keep pace with rapid evolution.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (clearml/clearml) · observed Aug 3, 2026
- GitHub forks (clearml/clearml) · observed Aug 3, 2026
- Last push (clearml/clearml) · observed Jul 27, 2026
- License file (Apache-2.0) · observed Aug 3, 2026
- Decision facts (enrichment) · observed Jul 14, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (stas00/ml-engineering) · observed Aug 17, 2026
- GitHub forks (stas00/ml-engineering) · observed Aug 17, 2026
- Last push (stas00/ml-engineering) · observed Aug 14, 2026
- License file (CC-BY-SA-4.0) · observed Aug 17, 2026
- Decision facts (enrichment) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: clearml 6.8k · ml-engineering 19k (synced Aug 3, 2026).
Common questions
- What is the difference between clearml and ml-engineering?
- clearml: MLOps/LLMOps solution for CI/CD in AI workloads. ml-engineering: Machine Learning Engineering Open Book. See the comparison table for live GitHub stats and shared categories.
- When should I choose clearml over ml-engineering?
- Choose clearml over ml-engineering when License: clearml is Apache-2.0, ml-engineering is CC-BY-SA-4.0; Tags unique to clearml: clearml, control, deep-learning, deeplearning; When you require a single platform for managing experiments, orchestrating pipelines, and serving models in your AI projects.
- When should I choose ml-engineering over clearml?
- Choose ml-engineering over clearml when License: ml-engineering is CC-BY-SA-4.0, clearml is Apache-2.0; Requirements: This resource is a documentation repository and does not have specific system requirements typical of software installations. Reading assumes availability of a僚; Tags unique to ml-engineering: debugging, gpus, inference, large language models; Also covers Developer Tools; - **Extensive Learning Resource**: If you are looking for a detailed read that covers a broad array of ML engineering practices and principles.
- When should I avoid clearml?
- Avoid if you need deep support for languages other than Python since ClearML is primarily built around Python Consider alternatives if your MLOps needs do not include a centralized orchestration platform, as ClearML emphasizes integrated solutions
- When should I avoid ml-engineering?
- - **Immediate Hands-On Code Snippets**: If you prefer a repository that provides specific code samples or tutorials rather than explanatory text. - **Vendor-Specific Tools Focus**: For users primarily focusing on tools from proprietary vendors where detailed, technical book content might not keep pace with rapid evolution.
- Is clearml or ml-engineering more popular on GitHub?
- ml-engineering has more GitHub stars (18,632 vs 6,805). Stars measure visibility, not whether either tool fits your constraints.
- Are clearml and ml-engineering open source?
- Yes - both are open-source projects on GitHub (clearml: Apache-2.0, ml-engineering: CC-BY-SA-4.0).
- Where can I find alternatives to clearml or ml-engineering?
- GraphCanon lists graph-backed alternatives at clearml alternatives and ml-engineering alternatives (clearml markdown twin, ml-engineering 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, clearml or ml-engineering?
- clearml: Active. ml-engineering: Very active. 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 clearml and ml-engineering?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: clearml trust report; ml-engineering trust report.