--- title: "amazon-sagemaker-examples vs geti_v2" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/aws-amazon-sagemaker-examples-vs-open-edge-platform-geti-v2" tools: ["aws-amazon-sagemaker-examples", "open-edge-platform-geti-v2"] --- # amazon-sagemaker-examples vs geti_v2 *GraphCanon updated Sep 20, 2026* ## Verdict Pick amazon-sagemaker-examples if jupyter notebooks for illustrating machine learning workflows on Amazon SageMaker; pick geti_v2 if geti_v2 is designed for developers who need to build computer vision models quickly using limited datasets. It supports TypeScript and integrates with frameworks like OpenVINO. [amazon-sagemaker-examples](https://sagemaker-examples.readthedocs.io) reports 11k GitHub stars, 7.0k forks, and 854 open issues, last pushed Sep 9, 2026. [geti_v2](https://docs.geti.intel.com/docs/2.0/user-guide/getting-started/introduction) has 483 stars, 51 forks, and 87 open issues, last pushed Jul 30, 2026. Figures are from public GitHub metadata via [amazon-sagemaker-examples's repository](https://github.com/aws/amazon-sagemaker-examples) and [geti_v2's repository](https://github.com/open-edge-platform/geti_v2). | | [amazon-sagemaker-examples](/tools/aws-amazon-sagemaker-examples.md) | [geti_v2](/tools/open-edge-platform-geti-v2.md) | | --- | --- | --- | | Tagline | Jupyter notebooks for building, training, and deploying ML models using Amazon SageMaker | Build computer vision models quickly with less data | | Stars | 10,990 | 483 | | Forks | 6,955 | 51 | | Open issues | 854 | 87 | | Language | Jupyter Notebook | TypeScript | | Adopt for | Jupyter notebooks for illustrating machine learning workflows on Amazon SageMaker | geti_v2 is designed for developers who need to build computer vision models quickly using limited datasets. It supports TypeScript and integrates with frameworks like OpenVINO. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0, allowing free use for any purpose with conditions on attribution and license preservation | The licensing type is listed as 'Other', implying that the license details should be closely reviewed for specific terms. | | Categories | Inference & Serving, Model Training | Computer Vision, 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) | [geti_v2](/tools/open-edge-platform-geti-v2.md) | | --- | --- | --- | | Maintenance | Active (82%) | Archived (8%) | | Days since push | 10d | 51d | | Archived on GitHub | No | Yes | | Open issues (now) | 854 | 87 | | Stars delta | +6 (30d) | -1 (30d) | | Open issues delta | +5 (30d) | +1 (30d) | | Full report | [trust report](/tools/aws-amazon-sagemaker-examples/trust.md) | [trust report](/tools/open-edge-platform-geti-v2/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: geti_v2 - **Pricing:** unknown - Pricing information is not provided. - **Requirements:** Min 0 GB RAM - **Adopt for:** geti_v2 is designed for developers who need to build computer vision models quickly using limited datasets. It supports TypeScript and integrates with frameworks like OpenVINO. - **License detail:** The licensing type is listed as 'Other', implying that the license details should be closely reviewed for specific terms. ## Choose when ### Choose amazon-sagemaker-examples if… - amazon-sagemaker-examples is primarily Jupyter Notebook; geti_v2 is TypeScript. - License: amazon-sagemaker-examples is Apache-2.0, geti_v2 is Other. - Tags unique to amazon-sagemaker-examples: aws, data-science, jupyter-notebook, machine-learning. - When you need examples specific to building models with Amazon SageMaker ### Choose geti_v2 if… - geti_v2 is primarily TypeScript; amazon-sagemaker-examples is Jupyter Notebook. - License: geti_v2 is Other, amazon-sagemaker-examples is Apache-2.0. - Pricing: Pricing information is not provided.. - Requirements: Min 0 GB RAM. - Tags unique to geti_v2: computer-vision, fine-tuning. - Also covers Computer Vision. - When you have a shortage of labeled data but still require high accuracy in your computer vision model. ## 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 geti_v2 - When you need to work with languages other than TypeScript, as geti_v2 is specifically designed for use with TypeScript environments. - In scenarios where you have abundant labeled data and can afford longer training times, which may not leverage the key advantage of geti_v2's efficiency in low-data conditions. ## Common questions ### What is the difference between amazon-sagemaker-examples and geti_v2? amazon-sagemaker-examples: Jupyter notebooks for building, training, and deploying ML models using Amazon SageMaker. geti_v2: Build computer vision models quickly with less data. See the comparison table for live GitHub stats and shared categories. ### When should I choose amazon-sagemaker-examples over geti_v2? Choose amazon-sagemaker-examples over geti_v2 when amazon-sagemaker-examples is primarily Jupyter Notebook; geti_v2 is TypeScript; License: amazon-sagemaker-examples is Apache-2.0, geti_v2 is Other; Tags unique to amazon-sagemaker-examples: aws, data-science, jupyter-notebook, machine-learning; When you need examples specific to building models with Amazon SageMaker. ### When should I choose geti_v2 over amazon-sagemaker-examples? Choose geti_v2 over amazon-sagemaker-examples when geti_v2 is primarily TypeScript; amazon-sagemaker-examples is Jupyter Notebook; License: geti_v2 is Other, amazon-sagemaker-examples is Apache-2.0; Pricing: Pricing information is not provided.; Requirements: Min 0 GB RAM; Tags unique to geti_v2: computer-vision, fine-tuning; Also covers Computer Vision; When you have a shortage of labeled data but still require high accuracy in your computer vision model. ### 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 geti_v2? When you need to work with languages other than TypeScript, as geti_v2 is specifically designed for use with TypeScript environments. In scenarios where you have abundant labeled data and can afford longer training times, which may not leverage the key advantage of geti_v2's efficiency in low-data conditions. ### Is amazon-sagemaker-examples or geti_v2 more popular on GitHub? amazon-sagemaker-examples has more GitHub stars (10,990 vs 483). Stars measure visibility, not whether either tool fits your constraints. ### Are amazon-sagemaker-examples and geti_v2 open source? Yes - both are open-source projects on GitHub (amazon-sagemaker-examples: Apache-2.0, geti_v2: Other). ### Where can I find alternatives to amazon-sagemaker-examples or geti_v2? GraphCanon lists graph-backed alternatives at [amazon-sagemaker-examples alternatives](/tools/aws-amazon-sagemaker-examples/alternatives) and [geti_v2 alternatives](/tools/open-edge-platform-geti-v2/alternatives) ([amazon-sagemaker-examples markdown twin](/tools/aws-amazon-sagemaker-examples/alternatives.md), [geti_v2 markdown twin](/tools/open-edge-platform-geti-v2/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-open-edge-platform-geti-v2.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 geti_v2? amazon-sagemaker-examples: Active. geti_v2: Archived. 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 geti_v2? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [amazon-sagemaker-examples trust report](/tools/aws-amazon-sagemaker-examples/trust); [geti_v2 trust report](/tools/open-edge-platform-geti-v2/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/_