--- title: "clearml vs Made-With-ML" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/clearml-clearml-vs-gokumohandas-made-with-ml" tools: ["clearml-clearml", "gokumohandas-made-with-ml"] --- # clearml vs Made-With-ML *GraphCanon updated Aug 14, 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 Made-With-ML if made-With-ML is about equipping developers with practical resources to design, develop, deploy and iterate on production-grade machine learning applications within their software engineering workflows. [clearml](https://clear.ml/docs) reports 6.8k GitHub stars, 785 forks, and 573 open issues, last pushed Jul 27, 2026. [Made-With-ML](https://madewithml.com) has 49k stars, 7.7k forks, and 26 open issues, last pushed Mar 4, 2026. Figures are from public GitHub metadata via [clearml's repository](https://github.com/clearml/clearml) and [Made-With-ML's repository](https://github.com/GokuMohandas/Made-With-ML). | | [clearml](/tools/clearml-clearml.md) | [Made-With-ML](/tools/gokumohandas-made-with-ml.md) | | --- | --- | --- | | Tagline | MLOps/LLMOps solution for CI/CD in AI workloads | Learn to develop, deploy and iterate on production-grade ML applications | | Stars | 6,805 | 49,074 | | Forks | 785 | 7,710 | | Open issues | 573 | 26 | | Language | Python | Jupyter Notebook | | 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. | Made-With-ML is about equipping developers with practical resources to design, develop, deploy and iterate on production-grade machine learning applications within their software engineering workflows. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | MIT | | 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) | [Made-With-ML](/tools/gokumohandas-made-with-ml.md) | | --- | --- | --- | | Maintenance | Active (82%) | Slowing (36%) | | Days since push | 7d | 162d | | Open issues (now) | 573 | 26 | | Stars delta | Unknown | +371 (30d) | | Open issues delta | Unknown | -1 (30d) | | Owner type | Organization | User | | Full report | [trust report](/tools/clearml-clearml/trust.md) | [trust report](/tools/gokumohandas-made-with-ml/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: Made-With-ML - **Requirements:** A foundational understanding of Python programming is required to fully benefit from the learning resources provided. - **Adopt for:** Made-With-ML is about equipping developers with practical resources to design, develop, deploy and iterate on production-grade machine learning applications within their software engineering workflows. ## Choose when ### Choose clearml if… - clearml is primarily Python; Made-With-ML is Jupyter Notebook. - License: clearml is Apache-2.0, Made-With-ML is MIT. - Tags unique to clearml: ai, clearml, control, deeplearning. - When you require a single platform for managing experiments, orchestrating pipelines, and serving models in your AI projects ### Choose Made-With-ML if… - Made-With-ML is primarily Jupyter Notebook; clearml is Python. - License: Made-With-ML is MIT, clearml is Apache-2.0. - Requirements: A foundational understanding of Python programming is required to fully benefit from the learning resources provided.. - Tags unique to Made-With-ML: data-engineering, data-quality, data-science, distributed-ml. - Also covers Developer Tools. - If you are looking for comprehensive tutorials that connect foundational ML concepts directly with hands-on coding practices using Python and PyTorch. ## 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 Made-With-ML - If you are looking for a niche-focused tool that caters specifically to a particular machine learning framework other than PyTorch. - For developers who already have strong backgrounds in MLOps and require highly specialized tools for managing production-grade ML deployments without additional educational support. ## Common questions ### What is the difference between clearml and Made-With-ML? clearml: MLOps/LLMOps solution for CI/CD in AI workloads. Made-With-ML: Learn to develop, deploy and iterate on production-grade ML applications. See the comparison table for live GitHub stats and shared categories. ### When should I choose clearml over Made-With-ML? Choose clearml over Made-With-ML when clearml is primarily Python; Made-With-ML is Jupyter Notebook; License: clearml is Apache-2.0, Made-With-ML is MIT; Tags unique to clearml: ai, clearml, control, deeplearning; When you require a single platform for managing experiments, orchestrating pipelines, and serving models in your AI projects. ### When should I choose Made-With-ML over clearml? Choose Made-With-ML over clearml when Made-With-ML is primarily Jupyter Notebook; clearml is Python; License: Made-With-ML is MIT, clearml is Apache-2.0; Requirements: A foundational understanding of Python programming is required to fully benefit from the learning resources provided.; Tags unique to Made-With-ML: data-engineering, data-quality, data-science, distributed-ml; Also covers Developer Tools; If you are looking for comprehensive tutorials that connect foundational ML concepts directly with hands-on coding practices using Python and PyTorch. ### 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 Made-With-ML? If you are looking for a niche-focused tool that caters specifically to a particular machine learning framework other than PyTorch. For developers who already have strong backgrounds in MLOps and require highly specialized tools for managing production-grade ML deployments without additional educational support. ### Is clearml or Made-With-ML more popular on GitHub? Made-With-ML has more GitHub stars (49,074 vs 6,805). Stars measure visibility, not whether either tool fits your constraints. ### Are clearml and Made-With-ML open source? Yes - both are open-source projects on GitHub (clearml: Apache-2.0, Made-With-ML: MIT). ### Where can I find alternatives to clearml or Made-With-ML? GraphCanon lists graph-backed alternatives at [clearml alternatives](/tools/clearml-clearml/alternatives) and [Made-With-ML alternatives](/tools/gokumohandas-made-with-ml/alternatives) ([clearml markdown twin](/tools/clearml-clearml/alternatives.md), [Made-With-ML markdown twin](/tools/gokumohandas-made-with-ml/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-gokumohandas-made-with-ml.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, clearml or Made-With-ML? clearml: Active. Made-With-ML: 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 clearml and Made-With-ML? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [clearml trust report](/tools/clearml-clearml/trust); [Made-With-ML trust report](/tools/gokumohandas-made-with-ml/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/_