--- title: "comet-examples vs llm-course" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/comet-ml-comet-examples-vs-mlabonne-llm-course" tools: ["comet-ml-comet-examples", "mlabonne-llm-course"] --- # comet-examples vs llm-course *GraphCanon updated Sep 27, 2026* ## Verdict Pick comet-examples if comet-examples is a collection of machine learning code demonstrations using Comet.ml. It focuses on deep learning algorithms and libraries, providing examples in Jupyter Notebook format; pick llm-course if llm-course provides a comprehensive curriculum on Large Language Models, including fundamental knowledge, building and deploying LLMs, and hands-on Colab notebooks. [comet-examples](https://github.com/comet-ml/comet-examples) reports 176 GitHub stars, 69 forks, and 27 open issues, last pushed Aug 12, 2026. [llm-course](https://mlabonne.github.io/blog/) has 83k stars, 9.7k forks, and 90 open issues, last pushed Feb 5, 2026. Figures are from public GitHub metadata via [comet-examples's repository](https://github.com/comet-ml/comet-examples) and [llm-course's repository](https://github.com/mlabonne/llm-course). | | [comet-examples](/tools/comet-ml-comet-examples.md) | [llm-course](/tools/mlabonne-llm-course.md) | | --- | --- | --- | | Tagline | Examples of Machine Learning code using Comet.ml | Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks. | | Stars | 176 | 83,011 | | Forks | 69 | 9,657 | | Open issues | 27 | 90 | | Language | Jupyter Notebook | - | | Adopt for | Comet-examples is a collection of machine learning code demonstrations using Comet.ml. It focuses on deep learning algorithms and libraries, providing examples in Jupyter Notebook format. | llm-course provides a comprehensive curriculum on Large Language Models, including fundamental knowledge, building and deploying LLMs, and hands-on Colab notebooks. | | Persona | - | - | | Runtime | - | - | | License | - | Apache-2.0 | | Categories | Evaluation & Observability, Model Training | Developer Tools, Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [comet-examples](/tools/comet-ml-comet-examples.md) | [llm-course](/tools/mlabonne-llm-course.md) | | --- | --- | --- | | Maintenance | Steady (60%) | Slowing (36%) | | Days since push | 45d | 224d | | Open issues (now) | 27 | 90 | | Stars delta | 0 (30d) | +1.5k (30d) | | Open issues delta | +1 (30d) | +4 (30d) | | Owner type | Organization | User | | Full report | [trust report](/tools/comet-ml-comet-examples/trust.md) | [trust report](/tools/mlabonne-llm-course/trust.md) | ## Shared compatibility - **Python**: [comet-examples](/tools/comet-ml-comet-examples.md) - Python runtime; [llm-course](/tools/mlabonne-llm-course.md) - Python runtime ## Decision facts: comet-examples - **Adopt for:** Comet-examples is a collection of machine learning code demonstrations using Comet.ml. It focuses on deep learning algorithms and libraries, providing examples in Jupyter Notebook format. ## Decision facts: llm-course - **Adopt for:** llm-course provides a comprehensive curriculum on Large Language Models, including fundamental knowledge, building and deploying LLMs, and hands-on Colab notebooks. ## Choose when ### Choose comet-examples if… - Tags unique to comet-examples: comet-ml, deep-learning-algorithms, machine-learning-platform, python. - When you are working with deep learning frameworks such as PyTorch or TensorFlow and want to integrate Comet.ml for experiment tracking and model management. - More recently updated (last pushed Aug 12, 2026). ### Choose llm-course if… - Tags unique to llm-course: course, large-language-models, llm, machine-learning. - Also covers Developer Tools, Inference & Serving, LLM Frameworks. - Use llm-course if you are looking for a structured learning path that includes both theoretical and practical aspects of LLMs, from fundamentals to deployment. ## When NOT to use comet-examples - If you are looking for a tool without third-party dependencies, as Comet-examples necessitates the use of Comet.ml which requires registration. - Avoid using this repository if your project does not need advanced experiment management features and simple code examples would suffice. ## When NOT to use llm-course - Avoid llm-course if you are seeking a course that focuses solely on theoretical aspects without practical implementation. - Do not use llm-course if you prefer a more formal certification program or a course that is part of a university curriculum. - Skip llm-course if you are looking for a tool that provides only code snippets or pre-built models without a structured learning path. ## Common questions ### What is the difference between comet-examples and llm-course? comet-examples: Examples of Machine Learning code using Comet.ml. 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 comet-examples over llm-course? Choose comet-examples over llm-course when Tags unique to comet-examples: comet-ml, deep-learning-algorithms, machine-learning-platform, python; When you are working with deep learning frameworks such as PyTorch or TensorFlow and want to integrate Comet.ml for experiment tracking and model management; More recently updated (last pushed Aug 12, 2026). ### When should I choose llm-course over comet-examples? Choose llm-course over comet-examples when Tags unique to llm-course: course, large-language-models, llm, machine-learning; Also covers Developer Tools, Inference & Serving, LLM Frameworks; Use llm-course if you are looking for a structured learning path that includes both theoretical and practical aspects of LLMs, from fundamentals to deployment. ### When should I avoid comet-examples? If you are looking for a tool without third-party dependencies, as Comet-examples necessitates the use of Comet.ml which requires registration. Avoid using this repository if your project does not need advanced experiment management features and simple code examples would suffice. ### When should I avoid llm-course? Avoid llm-course if you are seeking a course that focuses solely on theoretical aspects without practical implementation. Do not use llm-course if you prefer a more formal certification program or a course that is part of a university curriculum. Skip llm-course if you are looking for a tool that provides only code snippets or pre-built models without a structured learning path. ### Is comet-examples or llm-course more popular on GitHub? llm-course has more GitHub stars (83,011 vs 176). Stars measure visibility, not whether either tool fits your constraints. ### Are comet-examples and llm-course open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to comet-examples or llm-course? GraphCanon lists graph-backed alternatives at [comet-examples alternatives](/tools/comet-ml-comet-examples/alternatives) and [llm-course alternatives](/tools/mlabonne-llm-course/alternatives) ([comet-examples markdown twin](/tools/comet-ml-comet-examples/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/comet-ml-comet-examples-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, comet-examples or llm-course? comet-examples: Steady. 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 comet-examples and llm-course? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [comet-examples trust report](/tools/comet-ml-comet-examples/trust); [llm-course trust report](/tools/mlabonne-llm-course/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=comet-ml-comet-examples`](/api/graphcanon/graph?tool=comet-ml-comet-examples) - 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/_