--- title: "amazon-sagemaker-examples vs llm-course" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/aws-amazon-sagemaker-examples-vs-mlabonne-llm-course" tools: ["aws-amazon-sagemaker-examples", "mlabonne-llm-course"] --- # amazon-sagemaker-examples vs llm-course *GraphCanon updated Jul 15, 2026* ## Verdict Pick amazon-sagemaker-examples when tags unique to amazon-sagemaker-examples: aws, data-science, deep-learning, examples; pick llm-course when requirements: Course materials are available in Colab notebooks; access requires a Google account. [amazon-sagemaker-examples](https://sagemaker-examples.readthedocs.io) reports 11k GitHub stars, 7.0k forks, and 849 open issues, last pushed Jul 7, 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 [amazon-sagemaker-examples's repository](https://github.com/aws/amazon-sagemaker-examples) and [llm-course's repository](https://github.com/mlabonne/llm-course). | | [amazon-sagemaker-examples](/tools/aws-amazon-sagemaker-examples.md) | [llm-course](/tools/mlabonne-llm-course.md) | | --- | --- | --- | | Tagline | Example 📓 Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using 🧠 Amazon SageMaker. | Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks. | | Stars | 10,971 | 80,904 | | Forks | 6,969 | 9,424 | | Open issues | 849 | 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 | Apache-2.0 | Apache-2.0 | | Categories | Inference & Serving, Model Training | Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [amazon-sagemaker-examples](/tools/aws-amazon-sagemaker-examples.md) | [llm-course](/tools/mlabonne-llm-course.md) | | --- | --- | --- | | Maintenance | Active (82%) | Slowing (36%) | | Days since push | 7d | 159d | | Open issues (now) | 849 | 85 | | Owner type | Organization | User | | Full report | [trust report](/tools/aws-amazon-sagemaker-examples/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 amazon-sagemaker-examples if… - Tags unique to amazon-sagemaker-examples: aws, data-science, deep-learning, examples. - More recently updated (last pushed Jul 7, 2026). ### Choose llm-course if… - 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, LLM Frameworks. - - When you want a comprehensive roadmap for understanding large language models including fundamental knowledge ## When NOT to use amazon-sagemaker-examples - Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic. - 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 amazon-sagemaker-examples and llm-course? amazon-sagemaker-examples: Example 📓 Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using 🧠 Amazon SageMaker.. 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 amazon-sagemaker-examples over llm-course? Choose amazon-sagemaker-examples over llm-course when Tags unique to amazon-sagemaker-examples: aws, data-science, deep-learning, examples; More recently updated (last pushed Jul 7, 2026). ### When should I choose llm-course over amazon-sagemaker-examples? Choose llm-course over amazon-sagemaker-examples when 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, LLM Frameworks; - When you want a comprehensive roadmap for understanding large language models including fundamental knowledge. ### When should I avoid amazon-sagemaker-examples? Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic. 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 amazon-sagemaker-examples or llm-course more popular on GitHub? llm-course has more GitHub stars (80,904 vs 10,971). Stars measure visibility, not whether either tool fits your constraints. ### Are amazon-sagemaker-examples and llm-course open source? Yes - both are open-source projects on GitHub (amazon-sagemaker-examples: Apache-2.0, llm-course: Apache-2.0). ### Where can I find alternatives to amazon-sagemaker-examples or llm-course? GraphCanon lists graph-backed alternatives at [amazon-sagemaker-examples alternatives](/tools/aws-amazon-sagemaker-examples/alternatives) and [llm-course alternatives](/tools/mlabonne-llm-course/alternatives) ([amazon-sagemaker-examples markdown twin](/tools/aws-amazon-sagemaker-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/aws-amazon-sagemaker-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, amazon-sagemaker-examples or llm-course? amazon-sagemaker-examples: Active. 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 amazon-sagemaker-examples and llm-course? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [amazon-sagemaker-examples trust report](/tools/aws-amazon-sagemaker-examples/trust); [llm-course trust report](/tools/mlabonne-llm-course/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=aws-amazon-sagemaker-examples`](/api/graphcanon/graph?tool=aws-amazon-sagemaker-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/_