--- title: "clearml vs ml-engineering" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/clearml-clearml-vs-stas00-ml-engineering" tools: ["clearml-clearml", "stas00-ml-engineering"] --- # clearml vs ml-engineering *GraphCanon updated Aug 17, 2026* ## 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. [clearml](https://clear.ml/docs) reports 6.8k GitHub stars, 785 forks, and 573 open issues, last pushed Jul 27, 2026. [ml-engineering](https://stasosphere.com/machine-learning/) has 19k stars, 1.2k forks, and 3 open issues, last pushed Aug 14, 2026. Figures are from public GitHub metadata via [clearml's repository](https://github.com/clearml/clearml) and [ml-engineering's repository](https://github.com/stas00/ml-engineering). | | [clearml](/tools/clearml-clearml.md) | [ml-engineering](/tools/stas00-ml-engineering.md) | | --- | --- | --- | | Tagline | MLOps/LLMOps solution for CI/CD in AI workloads | Machine Learning Engineering Open Book | | Stars | 6,805 | 18,632 | | Forks | 785 | 1,200 | | Open issues | 573 | 3 | | Language | Python | Python | | Adopt for | 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 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 | - | - | | Runtime | - | - | | License | Apache-2.0 | CC-BY-SA-4.0 | | Categories | Inference & Serving, Model Training | Developer Tools, Inference & Serving, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [clearml](/tools/clearml-clearml.md) | [ml-engineering](/tools/stas00-ml-engineering.md) | | --- | --- | --- | | Maintenance | Active (82%) | Very active (96%) | | Days since push | 7d | 2d | | Open issues (now) | 573 | 3 | | Stars delta | Unknown | +216 (30d) | | Open issues delta | Unknown | +1 (30d) | | Owner type | Organization | User | | Full report | [trust report](/tools/clearml-clearml/trust.md) | [trust report](/tools/stas00-ml-engineering/trust.md) | ## Decision facts: clearml - **Adopt for:** ClearML is an MLOps LLMOps solution that streamlines AI workloads through comprehensive experiment management, data handling, pipeline orchestration, and model serving under one platform. ## Decision facts: ml-engineering - **Requirements:** This resource is a documentation repository and does not have specific system requirements typical of software installations. Reading assumes availability of a僚 - **Adopt for:** 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. ## Choose when ### 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 ### 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 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 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. ## 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](/tools/clearml-clearml/alternatives) and [ml-engineering alternatives](/tools/stas00-ml-engineering/alternatives) ([clearml markdown twin](/tools/clearml-clearml/alternatives.md), [ml-engineering markdown twin](/tools/stas00-ml-engineering/alternatives.md)), 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](/compare/clearml-clearml-vs-stas00-ml-engineering.md) 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](/tools/clearml-clearml/trust); [ml-engineering trust report](/tools/stas00-ml-engineering/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=clearml-clearml`](/api/graphcanon/graph?tool=clearml-clearml) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_