--- title: "Made-With-ML vs ml-engineering" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/gokumohandas-made-with-ml-vs-stas00-ml-engineering" tools: ["gokumohandas-made-with-ml", "stas00-ml-engineering"] --- # Made-With-ML vs ml-engineering *GraphCanon updated Aug 17, 2026* ## Verdict 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; 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. [Made-With-ML](https://madewithml.com) reports 49k GitHub stars, 7.7k forks, and 26 open issues, last pushed Mar 4, 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 [Made-With-ML's repository](https://github.com/GokuMohandas/Made-With-ML) and [ml-engineering's repository](https://github.com/stas00/ml-engineering). | | [Made-With-ML](/tools/gokumohandas-made-with-ml.md) | [ml-engineering](/tools/stas00-ml-engineering.md) | | --- | --- | --- | | Tagline | Learn to develop, deploy and iterate on production-grade ML applications | Machine Learning Engineering Open Book | | Stars | 49,074 | 18,632 | | Forks | 7,710 | 1,200 | | Open issues | 26 | 3 | | Language | Jupyter Notebook | Python | | 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. | 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 | MIT | CC-BY-SA-4.0 | | Categories | Developer Tools, Inference & Serving, Model Training | Developer Tools, Inference & Serving, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [Made-With-ML](/tools/gokumohandas-made-with-ml.md) | [ml-engineering](/tools/stas00-ml-engineering.md) | | --- | --- | --- | | Maintenance | Slowing (36%) | Very active (96%) | | Days since push | 162d | 2d | | Open issues (now) | 26 | 3 | | Stars delta | +371 (30d) | +216 (30d) | | Open issues delta | -1 (30d) | +1 (30d) | | Full report | [trust report](/tools/gokumohandas-made-with-ml/trust.md) | [trust report](/tools/stas00-ml-engineering/trust.md) | ## 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. ## 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 Made-With-ML if… - Made-With-ML is primarily Jupyter Notebook; ml-engineering is Python. - License: Made-With-ML is MIT, ml-engineering is CC-BY-SA-4.0. - 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, deep-learning. - If you are looking for comprehensive tutorials that connect foundational ML concepts directly with hands-on coding practices using Python and PyTorch. ### Choose ml-engineering if… - ml-engineering is primarily Python; Made-With-ML is Jupyter Notebook. - License: ml-engineering is CC-BY-SA-4.0, Made-With-ML is MIT. - 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: ai, debugging, gpus, inference. - - **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 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. ## 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 Made-With-ML and ml-engineering? Made-With-ML: Learn to develop, deploy and iterate on production-grade ML applications. ml-engineering: Machine Learning Engineering Open Book. See the comparison table for live GitHub stats and shared categories. ### When should I choose Made-With-ML over ml-engineering? Choose Made-With-ML over ml-engineering when Made-With-ML is primarily Jupyter Notebook; ml-engineering is Python; License: Made-With-ML is MIT, ml-engineering is CC-BY-SA-4.0; 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, deep-learning; 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 choose ml-engineering over Made-With-ML? Choose ml-engineering over Made-With-ML when ml-engineering is primarily Python; Made-With-ML is Jupyter Notebook; License: ml-engineering is CC-BY-SA-4.0, Made-With-ML is MIT; 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: ai, debugging, gpus, inference; - **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 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. ### 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 Made-With-ML or ml-engineering more popular on GitHub? Made-With-ML has more GitHub stars (49,074 vs 18,632). Stars measure visibility, not whether either tool fits your constraints. ### Are Made-With-ML and ml-engineering open source? Yes - both are open-source projects on GitHub (Made-With-ML: MIT, ml-engineering: CC-BY-SA-4.0). ### Where can I find alternatives to Made-With-ML or ml-engineering? GraphCanon lists graph-backed alternatives at [Made-With-ML alternatives](/tools/gokumohandas-made-with-ml/alternatives) and [ml-engineering alternatives](/tools/stas00-ml-engineering/alternatives) ([Made-With-ML markdown twin](/tools/gokumohandas-made-with-ml/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/gokumohandas-made-with-ml-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, Made-With-ML or ml-engineering? Made-With-ML: Slowing. 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 Made-With-ML and ml-engineering? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [Made-With-ML trust report](/tools/gokumohandas-made-with-ml/trust); [ml-engineering trust report](/tools/stas00-ml-engineering/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=gokumohandas-made-with-ml`](/api/graphcanon/graph?tool=gokumohandas-made-with-ml) - 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/_