--- title: "amazon-sagemaker-examples vs awesome-mlops" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/aws-amazon-sagemaker-examples-vs-visenger-awesome-mlops" tools: ["aws-amazon-sagemaker-examples", "visenger-awesome-mlops"] --- # amazon-sagemaker-examples vs awesome-mlops *GraphCanon updated Sep 20, 2026* ## Verdict Pick amazon-sagemaker-examples if jupyter notebooks for illustrating machine learning workflows on Amazon SageMaker; pick awesome-mlops if awesome-mlops curates MLOps resources focusing on diverse deployment strategies and tooling. [amazon-sagemaker-examples](https://sagemaker-examples.readthedocs.io) reports 11k GitHub stars, 7.0k forks, and 854 open issues, last pushed Sep 9, 2026. [awesome-mlops](https://ml-ops.org) has 14k stars, 2.1k forks, and 43 open issues, last pushed Nov 21, 2024. Figures are from public GitHub metadata via [amazon-sagemaker-examples's repository](https://github.com/aws/amazon-sagemaker-examples) and [awesome-mlops's repository](https://github.com/visenger/awesome-mlops). | | [amazon-sagemaker-examples](/tools/aws-amazon-sagemaker-examples.md) | [awesome-mlops](/tools/visenger-awesome-mlops.md) | | --- | --- | --- | | Tagline | Jupyter notebooks for building, training, and deploying ML models using Amazon SageMaker | A curated list of references for MLOps | | Stars | 10,990 | 14,183 | | Forks | 6,955 | 2,110 | | Open issues | 854 | 43 | | Language | Jupyter Notebook | - | | Adopt for | Jupyter notebooks for illustrating machine learning workflows on Amazon SageMaker | awesome-mlops curates MLOps resources focusing on diverse deployment strategies and tooling. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0, allowing free use for any purpose with conditions on attribution and license preservation | - | | Categories | Inference & Serving, Model Training | Inference & Serving, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [amazon-sagemaker-examples](/tools/aws-amazon-sagemaker-examples.md) | [awesome-mlops](/tools/visenger-awesome-mlops.md) | | --- | --- | --- | | Maintenance | Active (82%) | Dormant (18%) | | Days since push | 10d | 651d | | Open issues (now) | 854 | 43 | | Stars delta | +6 (30d) | +56 (30d) | | Open issues delta | +5 (30d) | -1 (30d) | | Owner type | Organization | User | | Full report | [trust report](/tools/aws-amazon-sagemaker-examples/trust.md) | [trust report](/tools/visenger-awesome-mlops/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: awesome-mlops - **Adopt for:** awesome-mlops curates MLOps resources focusing on diverse deployment strategies and tooling. ## Choose when ### Choose amazon-sagemaker-examples if… - Tags unique to amazon-sagemaker-examples: aws, deep-learning, inference, jupyter-notebook. - When you need examples specific to building models with Amazon SageMaker - More recently updated (last pushed Sep 9, 2026). ### Choose awesome-mlops if… - Tags unique to awesome-mlops: ai, devops, engineering, federated-learning. - If you need references covering online training and inference service architecture patterns, consider awesome-mlops. - More GitHub stars (14k vs 11k) - visibility, not fit. ## 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 awesome-mlops - Avoid if focused solely on a single MLOps tool or framework as this is a broad resource list. - Not suitable for those seeking end-to-end support beyond references, like hands-on deployment assistance. ## Common questions ### What is the difference between amazon-sagemaker-examples and awesome-mlops? amazon-sagemaker-examples: Jupyter notebooks for building, training, and deploying ML models using Amazon SageMaker. awesome-mlops: A curated list of references for MLOps. See the comparison table for live GitHub stats and shared categories. ### When should I choose amazon-sagemaker-examples over awesome-mlops? Choose amazon-sagemaker-examples over awesome-mlops when Tags unique to amazon-sagemaker-examples: aws, deep-learning, inference, jupyter-notebook; When you need examples specific to building models with Amazon SageMaker; More recently updated (last pushed Sep 9, 2026). ### When should I choose awesome-mlops over amazon-sagemaker-examples? Choose awesome-mlops over amazon-sagemaker-examples when Tags unique to awesome-mlops: ai, devops, engineering, federated-learning; If you need references covering online training and inference service architecture patterns, consider awesome-mlops; More GitHub stars (14k vs 11k) - visibility, not fit. ### 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 awesome-mlops? Avoid if focused solely on a single MLOps tool or framework as this is a broad resource list. Not suitable for those seeking end-to-end support beyond references, like hands-on deployment assistance. ### Is amazon-sagemaker-examples or awesome-mlops more popular on GitHub? awesome-mlops has more GitHub stars (14,183 vs 10,990). Stars measure visibility, not whether either tool fits your constraints. ### Are amazon-sagemaker-examples and awesome-mlops open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to amazon-sagemaker-examples or awesome-mlops? GraphCanon lists graph-backed alternatives at [amazon-sagemaker-examples alternatives](/tools/aws-amazon-sagemaker-examples/alternatives) and [awesome-mlops alternatives](/tools/visenger-awesome-mlops/alternatives) ([amazon-sagemaker-examples markdown twin](/tools/aws-amazon-sagemaker-examples/alternatives.md), [awesome-mlops markdown twin](/tools/visenger-awesome-mlops/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-visenger-awesome-mlops.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 awesome-mlops? amazon-sagemaker-examples: Active. awesome-mlops: Dormant. 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 awesome-mlops? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [amazon-sagemaker-examples trust report](/tools/aws-amazon-sagemaker-examples/trust); [awesome-mlops trust report](/tools/visenger-awesome-mlops/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/_