--- title: "amazon-sagemaker-examples vs Large-Language-Model-Notebooks-Course" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/aws-amazon-sagemaker-examples-vs-peremartra-large-language-model-notebooks-course" tools: ["aws-amazon-sagemaker-examples", "peremartra-large-language-model-notebooks-course"] --- # amazon-sagemaker-examples vs Large-Language-Model-Notebooks-Course *GraphCanon updated Sep 20, 2026* ## Verdict Pick amazon-sagemaker-examples if jupyter notebooks for illustrating machine learning workflows on Amazon SageMaker; pick Large-Language-Model-Notebooks-Course if a developing, comprehensive hands-on course covering a broad array of LLM tools and applications from OpenAI and Hugging Face. [amazon-sagemaker-examples](https://sagemaker-examples.readthedocs.io) reports 11k GitHub stars, 7.0k forks, and 854 open issues, last pushed Sep 9, 2026. [Large-Language-Model-Notebooks-Course](https://medium.com/@peremartra/list/large-language-models-practical-course-66b4ce5943ce) has 1.8k stars, 450 forks, and 0 open issues, last pushed May 28, 2026. Figures are from public GitHub metadata via [amazon-sagemaker-examples's repository](https://github.com/aws/amazon-sagemaker-examples) and [Large-Language-Model-Notebooks-Course's repository](https://github.com/peremartra/Large-Language-Model-Notebooks-Course). | | [amazon-sagemaker-examples](/tools/aws-amazon-sagemaker-examples.md) | [Large-Language-Model-Notebooks-Course](/tools/peremartra-large-language-model-notebooks-course.md) | | --- | --- | --- | | Tagline | Jupyter notebooks for building, training, and deploying ML models using Amazon SageMaker | Practical course about Large Language Models | | Stars | 10,990 | 1,824 | | Forks | 6,955 | 450 | | Open issues | 854 | 0 | | Language | Jupyter Notebook | Jupyter Notebook | | Adopt for | Jupyter notebooks for illustrating machine learning workflows on Amazon SageMaker | A developing, comprehensive hands-on course covering a broad array of LLM tools and applications from OpenAI and Hugging Face. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0, allowing free use for any purpose with conditions on attribution and license preservation | MIT | | 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) | [Large-Language-Model-Notebooks-Course](/tools/peremartra-large-language-model-notebooks-course.md) | | --- | --- | --- | | Maintenance | Active (82%) | Slowing (36%) | | Days since push | 10d | 114d | | Open issues (now) | 854 | 0 | | Stars delta | +6 (30d) | +3 (30d) | | Open issues delta | +5 (30d) | 0 (30d) | | Owner type | Organization | User | | Full report | [trust report](/tools/aws-amazon-sagemaker-examples/trust.md) | [trust report](/tools/peremartra-large-language-model-notebooks-course/trust.md) | ## Decision facts: amazon-sagemaker-examples - **Adopt for:** Jupyter notebooks for illustrating machine learning workflows on Amazon SageMaker - **License detail:** Apache-2.0, allowing free use for any purpose with conditions on attribution and license preservation ## Decision facts: Large-Language-Model-Notebooks-Course - **Adopt for:** A developing, comprehensive hands-on course covering a broad array of LLM tools and applications from OpenAI and Hugging Face. ## Choose when ### Choose amazon-sagemaker-examples if… - License: amazon-sagemaker-examples is Apache-2.0, Large-Language-Model-Notebooks-Course is MIT. - Tags unique to amazon-sagemaker-examples: aws, data-science, deep-learning, inference. - When you need examples specific to building models with Amazon SageMaker ### Choose Large-Language-Model-Notebooks-Course if… - License: Large-Language-Model-Notebooks-Course is MIT, amazon-sagemaker-examples is Apache-2.0. - Tags unique to Large-Language-Model-Notebooks-Course: chatbots, fine-tuning-llm, huggingface, langchain. - Also covers Evaluation & Observability, LLM Frameworks. - You're seeking an evolving curriculum with projects that apply Large Language Model techniques from various libraries. ## When NOT to use amazon-sagemaker-examples - For non-AWS environments where cost and integration complexities could outweigh benefits - If seeking open-source tools without ties to a single cloud provider ## When NOT to use Large-Language-Model-Notebooks-Course - Seeking a complete, finalized course where all content is available for immediate use without future updates. - Looking exclusively for theory; the course emphasizes practical application over theoretical depth. ## Common questions ### What is the difference between amazon-sagemaker-examples and Large-Language-Model-Notebooks-Course? amazon-sagemaker-examples: Jupyter notebooks for building, training, and deploying ML models using Amazon SageMaker. Large-Language-Model-Notebooks-Course: Practical course about Large Language Models. See the comparison table for live GitHub stats and shared categories. ### When should I choose amazon-sagemaker-examples over Large-Language-Model-Notebooks-Course? Choose amazon-sagemaker-examples over Large-Language-Model-Notebooks-Course when License: amazon-sagemaker-examples is Apache-2.0, Large-Language-Model-Notebooks-Course is MIT; Tags unique to amazon-sagemaker-examples: aws, data-science, deep-learning, inference; When you need examples specific to building models with Amazon SageMaker. ### When should I choose Large-Language-Model-Notebooks-Course over amazon-sagemaker-examples? Choose Large-Language-Model-Notebooks-Course over amazon-sagemaker-examples when License: Large-Language-Model-Notebooks-Course is MIT, amazon-sagemaker-examples is Apache-2.0; Tags unique to Large-Language-Model-Notebooks-Course: chatbots, fine-tuning-llm, huggingface, langchain; Also covers Evaluation & Observability, LLM Frameworks; You're seeking an evolving curriculum with projects that apply Large Language Model techniques from various libraries. ### When should I avoid amazon-sagemaker-examples? For non-AWS environments where cost and integration complexities could outweigh benefits If seeking open-source tools without ties to a single cloud provider ### When should I avoid Large-Language-Model-Notebooks-Course? Seeking a complete, finalized course where all content is available for immediate use without future updates. Looking exclusively for theory; the course emphasizes practical application over theoretical depth. ### Is amazon-sagemaker-examples or Large-Language-Model-Notebooks-Course more popular on GitHub? amazon-sagemaker-examples has more GitHub stars (10,990 vs 1,824). Stars measure visibility, not whether either tool fits your constraints. ### Are amazon-sagemaker-examples and Large-Language-Model-Notebooks-Course open source? Yes - both are open-source projects on GitHub (amazon-sagemaker-examples: Apache-2.0, Large-Language-Model-Notebooks-Course: MIT). ### Where can I find alternatives to amazon-sagemaker-examples or Large-Language-Model-Notebooks-Course? GraphCanon lists graph-backed alternatives at [amazon-sagemaker-examples alternatives](/tools/aws-amazon-sagemaker-examples/alternatives) and [Large-Language-Model-Notebooks-Course alternatives](/tools/peremartra-large-language-model-notebooks-course/alternatives) ([amazon-sagemaker-examples markdown twin](/tools/aws-amazon-sagemaker-examples/alternatives.md), [Large-Language-Model-Notebooks-Course markdown twin](/tools/peremartra-large-language-model-notebooks-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-peremartra-large-language-model-notebooks-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 Large-Language-Model-Notebooks-Course? amazon-sagemaker-examples: Active. Large-Language-Model-Notebooks-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 Large-Language-Model-Notebooks-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); [Large-Language-Model-Notebooks-Course trust report](/tools/peremartra-large-language-model-notebooks-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/_