--- title: "Failed-ML vs llm-course" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/kennethleungty-failed-ml-vs-mlabonne-llm-course" tools: ["kennethleungty-failed-ml", "mlabonne-llm-course"] --- # Failed-ML vs llm-course *GraphCanon updated Jul 11, 2026* ## Verdict Pick Failed-ML when license: Failed-ML is MIT, llm-course is Apache-2.0; pick llm-course when license: llm-course is Apache-2.0, Failed-ML is MIT. [Failed-ML](https://towardsdatascience.com/when-ai-goes-astray-high-profile-machine-learning-mishaps-in-the-real-world-26bd58692195) reports 753 GitHub stars, 51 forks, and 0 open issues, last pushed Jun 14, 2024. [llm-course](https://mlabonne.github.io/blog/) has 81k stars, 9.4k forks, and 84 open issues, last pushed Feb 5, 2026. Figures are from public GitHub metadata via [Failed-ML's repository](https://github.com/kennethleungty/Failed-ML) and [llm-course's repository](https://github.com/mlabonne/llm-course). | | [Failed-ML](/tools/kennethleungty-failed-ml.md) | [llm-course](/tools/mlabonne-llm-course.md) | | --- | --- | --- | | Tagline | Compilation of high-profile real-world examples of failed machine learning projects | Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks. | | Stars | 753 | 80,839 | | Forks | 51 | 9,421 | | Open issues | 0 | 84 | | Language | - | - | | 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 | Computer Vision, LLM Frameworks, Model Training | Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [Failed-ML](/tools/kennethleungty-failed-ml.md) | [llm-course](/tools/mlabonne-llm-course.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Slowing (36%) | | Days since push | 757d | 155d | | Open issues (now) | 0 | 84 | | Full report | [trust report](/tools/kennethleungty-failed-ml/trust.md) | [trust report](/tools/mlabonne-llm-course/trust.md) | ## 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 Failed-ML if… - License: Failed-ML is MIT, llm-course is Apache-2.0. - Tags unique to Failed-ML: ai, artificial-intelligence, classification, computer-vision. - Also covers Computer Vision. ### Choose llm-course if… - License: llm-course is Apache-2.0, Failed-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, machine learning. - Also covers Evaluation & Observability, Inference & Serving. - - When you want a comprehensive roadmap for understanding large language models including fundamental knowledge ## When NOT to use Failed-ML - Last GitHub push was 758 days ago (dormant maintenance, Jun 14, 2024). Validate activity before betting a new project on Failed-ML. - 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 Failed-ML and llm-course? Failed-ML: Compilation of high-profile real-world examples of failed machine learning projects. 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 Failed-ML over llm-course? Choose Failed-ML over llm-course when License: Failed-ML is MIT, llm-course is Apache-2.0; Tags unique to Failed-ML: ai, artificial-intelligence, classification, computer-vision; Also covers Computer Vision. ### When should I choose llm-course over Failed-ML? Choose llm-course over Failed-ML when License: llm-course is Apache-2.0, Failed-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, machine learning; Also covers Evaluation & Observability, Inference & Serving; - When you want a comprehensive roadmap for understanding large language models including fundamental knowledge. ### When should I avoid Failed-ML? Last GitHub push was 758 days ago (dormant maintenance, Jun 14, 2024). Validate activity before betting a new project on Failed-ML. 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 Failed-ML or llm-course more popular on GitHub? llm-course has more GitHub stars (80,839 vs 753). Stars measure visibility, not whether either tool fits your constraints. ### Are Failed-ML and llm-course open source? Yes - both are open-source projects on GitHub (Failed-ML: MIT, llm-course: Apache-2.0). ### Where can I find alternatives to Failed-ML or llm-course? GraphCanon lists graph-backed alternatives at [Failed-ML alternatives](/tools/kennethleungty-failed-ml/alternatives) and [llm-course alternatives](/tools/mlabonne-llm-course/alternatives) ([Failed-ML markdown twin](/tools/kennethleungty-failed-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/kennethleungty-failed-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, Failed-ML or llm-course? Failed-ML: Dormant. 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 Failed-ML and llm-course? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [Failed-ML trust report](/tools/kennethleungty-failed-ml/trust); [llm-course trust report](/tools/mlabonne-llm-course/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=kennethleungty-failed-ml`](/api/graphcanon/graph?tool=kennethleungty-failed-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/_