--- title: "Made-With-ML vs llm-course" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/gokumohandas-made-with-ml-vs-mlabonne-llm-course" tools: ["gokumohandas-made-with-ml", "mlabonne-llm-course"] --- # Made-With-ML vs llm-course *GraphCanon updated Jul 15, 2026* ## Verdict Pick Made-With-ML when license: Made-With-ML is MIT, llm-course is Apache-2.0; pick llm-course when license: llm-course is Apache-2.0, Made-With-ML is MIT. [Made-With-ML](https://madewithml.com) reports 49k GitHub stars, 7.7k forks, and 27 open issues, last pushed Mar 4, 2026. [llm-course](https://mlabonne.github.io/blog/) has 81k stars, 9.4k forks, and 85 open issues, last pushed Feb 5, 2026. Figures are from public GitHub metadata via [Made-With-ML's repository](https://github.com/GokuMohandas/Made-With-ML) and [llm-course's repository](https://github.com/mlabonne/llm-course). | | [Made-With-ML](/tools/gokumohandas-made-with-ml.md) | [llm-course](/tools/mlabonne-llm-course.md) | | --- | --- | --- | | Tagline | Learn how to develop, deploy and iterate on production-grade ML applications. | Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks. | | Stars | 48,703 | 80,904 | | Forks | 7,661 | 9,424 | | Open issues | 27 | 85 | | Language | Jupyter Notebook | - | | Adopt for | - | The llm-course provides a comprehensive guided course on Large Language Models (LLMs), divided into three parts: LLM Fundamentals, The LLM Scientist, and The LLM Engineer. It includes resources such as Colab notebooks to | | Persona | - | - | | Runtime | - | - | | License | MIT | Apache-2.0 | | Categories | AI Agents, LLM Frameworks, Model Training | Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [Made-With-ML](/tools/gokumohandas-made-with-ml.md) | [llm-course](/tools/mlabonne-llm-course.md) | | --- | --- | --- | | Days since push | 132d | 159d | | Open issues (now) | 27 | 85 | | Full report | [trust report](/tools/gokumohandas-made-with-ml/trust.md) | [trust report](/tools/mlabonne-llm-course/trust.md) | ## Shared compatibility - **Python**: [Made-With-ML](/tools/gokumohandas-made-with-ml.md) - Python runtime; [llm-course](/tools/mlabonne-llm-course.md) - Python runtime ## Decision facts: llm-course - **Requirements:** Course materials are available in Colab notebooks; access requires a Google account - **Adopt for:** The llm-course provides a comprehensive guided course on Large Language Models (LLMs), divided into three parts: LLM Fundamentals, The LLM Scientist, and The LLM Engineer. It includes resources such as Colab notebooks to - **License detail:** Apache-2.0 ## Choose when ### Choose Made-With-ML if… - License: Made-With-ML is MIT, llm-course is Apache-2.0. - Tags unique to Made-With-ML: data-engineering, data-quality, data-science, deep-learning. - Also covers AI Agents. ### Choose llm-course if… - License: llm-course is Apache-2.0, Made-With-ML is MIT. - Requirements: Course materials are available in Colab notebooks; access requires a Google account. - Tags unique to llm-course: colab-notebooks, course, large-language-models, roadmap. - Also covers Evaluation & Observability, Inference & Serving. - - When you want a comprehensive roadmap for understanding large language models including fundamental knowledge ## When NOT to use Made-With-ML - Last GitHub push was 132 days ago (slowing maintenance, Mar 4, 2026). Validate activity before betting a new project on Made-With-ML. - AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism. - LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. - Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. ## When NOT to use llm-course - - If you only require a quick introduction to LLMs without deep dive into core components - - When you prefer working directly with commercial platforms that provide complete services rather than following detailed steps on building and deploying models yourself through this course's open,DI ## Common questions ### What is the difference between Made-With-ML and llm-course? Made-With-ML: Learn how to develop, deploy and iterate on production-grade ML applications.. llm-course: Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks.. See the comparison table for live GitHub stats and shared categories. ### When should I choose Made-With-ML over llm-course? Choose Made-With-ML over llm-course when License: Made-With-ML is MIT, llm-course is Apache-2.0; Tags unique to Made-With-ML: data-engineering, data-quality, data-science, deep-learning; Also covers AI Agents. ### When should I choose llm-course over Made-With-ML? Choose llm-course over Made-With-ML when License: llm-course is Apache-2.0, Made-With-ML is MIT; Requirements: Course materials are available in Colab notebooks; access requires a Google account; Tags unique to llm-course: colab-notebooks, course, large-language-models, roadmap; Also covers Evaluation & Observability, Inference & Serving; - When you want a comprehensive roadmap for understanding large language models including fundamental knowledge. ### When should I avoid Made-With-ML? Last GitHub push was 132 days ago (slowing maintenance, Mar 4, 2026). Validate activity before betting a new project on Made-With-ML. AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism. LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. ### When should I avoid llm-course? - If you only require a quick introduction to LLMs without deep dive into core components - When you prefer working directly with commercial platforms that provide complete services rather than following detailed steps on building and deploying models yourself through this course's open,DI ### Is Made-With-ML or llm-course more popular on GitHub? llm-course has more GitHub stars (80,904 vs 48,703). Stars measure visibility, not whether either tool fits your constraints. ### Are Made-With-ML and llm-course open source? Yes - both are open-source projects on GitHub (Made-With-ML: MIT, llm-course: Apache-2.0). ### Where can I find alternatives to Made-With-ML or llm-course? GraphCanon lists graph-backed alternatives at [Made-With-ML alternatives](/tools/gokumohandas-made-with-ml/alternatives) and [llm-course alternatives](/tools/mlabonne-llm-course/alternatives) ([Made-With-ML markdown twin](/tools/gokumohandas-made-with-ml/alternatives.md), [llm-course markdown twin](/tools/mlabonne-llm-course/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-mlabonne-llm-course.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 llm-course? Made-With-ML: Slowing. llm-course: 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 Made-With-ML and llm-course? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [Made-With-ML trust report](/tools/gokumohandas-made-with-ml/trust); [llm-course trust report](/tools/mlabonne-llm-course/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/_