AWS sagemaker documentation change
Summary
Added a new index documentation page for Amazon SageMaker AI with sections covering automation, data work, model work, responsible AI, references, and getting started links.
Security assessment
The change introduces a new documentation index page that includes a 'Responsible AI' section with a link to 'Configure Security in Amazon SageMaker AI'. This adds documentation about security features by providing a reference to security configuration guidance, but there is no evidence of addressing a specific security vulnerability or incident.
Diff
diff --git a/sagemaker/index.md b/sagemaker/index.md index 8b1378917..4b8682cd8 100644 --- a//sagemaker/index.md +++ b//sagemaker/index.md @@ -0,0 +1,55 @@ +# Amazon SageMaker AI Documentation + +Amazon SageMaker AI is a fully managed machine learning service. With Amazon SageMaker AI, data scientists and developers can quickly build and train machine learning models, and then deploy them into a production-ready hosted environment. + +## Automation and Environments + +### [Get Started Onboard to the Amazon SageMaker AI role and domain. ](/sagemaker/latest/dg/gs.html?icmpid=docs_sagemaker_lp) ### [Use Automated ML Learn how to automate machine learning from start to finish. ](/sagemaker/latest/dg/use-auto-ml.html?icmpid=docs_sagemaker_lp) ### [Use Machine Learning Environments Learn about machine learning environments that Amazon SageMaker AI offers. ](/sagemaker/latest/dg/machine-learning-environments.html) + +## Work with Data + +### [Label Data with a Human-in-the-loop Learn how to use a human-in-the-loop to help label data more accurately. ](/sagemaker/latest/dg/data-label.html) ### [Prepare Data Learn how to prepare data for machine learning. ](/sagemaker/latest/dg/data-prep.html) ### [Process Data Learn how to process data for machine learning. ](/sagemaker/latest/dg/processing-job.html) ### [Create, Store, and Share Features Learn how to create, store, and share extracted data signals (features) for machine learning. ](/sagemaker/latest/dg/feature-store.html) + +## Work with Models + +### [Train Models Learn how to train your machine learning models. ](/sagemaker/latest/dg/train-model.html) ### [Deploy Models for Inference Learn how to deploy your machine learning models for inference. ](/sagemaker/latest/dg/deploy-model.html) ### [Implement MLOps Learn how to implement machine learning operations on Amazon SageMaker AI. ](/sagemaker/latest/dg/mlops.html) ### [Monitor Data and Model Quality Learn how to monitor data and model quality. ](/sagemaker/latest/dg/model-monitor.html) ### [Use Docker Containers to Build Models Learn how to use Docker containers to build your machine learning models. ](/sagemaker/latest/dg/docker-containers.html) + +## Responsible AI + +### [Detect Bias and Understand Explanations Learn how to detect bias and understand explanations in machine learning models. ](/sagemaker/latest/dg/model-explainability.html) ### [Use Governance Learn how to use governance to document and track model performance. ](/sagemaker/latest/dg/governance.html) ### [Configure Security in Amazon SageMaker AI Learn how to secure resources in Amazon SageMaker AI. ](/sagemaker/latest/dg/security.html) + +## Reference + +### [API Reference Describes all of the API operations for Amazon SageMaker AI in detail. ](/sagemaker/latest/APIReference/Welcome.html?icmpid=docs_sagemaker_lp) + +[HTML](/sagemaker/latest/APIReference/Welcome.html?icmpid=docs_sagemaker_lp) [PDF](/sagemaker/latest/APIReference/sagemaker-api.pdf?icmpid=docs_sagemaker_lp) + +### [Amazon SageMaker AI Python SDK Use the Amazon SageMaker AI Python SDK library to train and deploy models using popular deep learning frameworks and algorithms. ](https://sagemaker.readthedocs.io/en/stable/index.html) + +[HTML](https://sagemaker.readthedocs.io/en/stable/index.html) [GitHub](https://github.com/aws/sagemaker-python-sdk/tree/f01fcae617ce2430804c0712a71e0c0234fc1967) + +### [AWS SDK for Python (Boto3) Use the AWS SDK for Python (Boto3) to format model data and build applications to build, train, and deploy machine learning models. ](https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/sagemaker.html?icmpid=docs_sagemaker_lp) + +[Instalar](http://github.com/boto/boto#installation) [SageMaker](https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/sagemaker.html?icmpid=docs_sagemaker_lp) [SageMakerRuntime](https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/sagemaker-runtime.html?icmpid=docs_sagemaker_lp) + +## Get Started + + * [Create, Train, and Deploy a Simple Machine Learning Model](https://docs.aws.amazon.com/sagemaker/latest/dg/gs.html?icmpid=docs_sagemaker_lp) + * [AWS Quick Start - Predictive Data Science with Amazon SageMaker and a Data Lake on AWS ](https://aws.amazon.com/quickstart/architecture/predictive-data-science-sagemaker-and-data-lake/?icmpid=docs_sagemaker_lp) + + + +## Related Info + + * [Product Information ](https://aws.amazon.com/sagemaker/?icmpid=docs_sagemaker_lp) + * [ Pricing](https://aws.amazon.com/sagemaker/pricing/?icmpid=docs_sagemaker_lp) + * [ FAQs](https://aws.amazon.com/sagemaker/faqs/?icmpid=docs_sagemaker_lp) + * [ AWS Machine Learning Blog](https://aws.amazon.com/blogs/machine-learning/?icmpid=docs_sagemaker_lp) + * [Amazon SageMaker Forum](https://forums.aws.amazon.com/forum.jspa?forumID=285) + * [SDKs & Tools](https://aws.amazon.com/tools/) + * [AWS General Reference](/general/latest/gr/Welcome.html?icmpid=docs_sagemaker_lp) + * [AWS Training](https://aws.amazon.com/training/learning-paths/machine-learning/?icmpid=docs_sagemaker_lp) + * [ Machine Learning Customers and Case Studies](https://aws.amazon.com/machine-learning/customers/?icmpid=docs_sagemaker_lp) + * [ AWS Whitepapers](https://aws.amazon.com/whitepapers/?icmpid=docs_sagemaker_lp#machine-learning) + * [Contact Us ](https://aws.amazon.com/contact-us/?icmpid=docs_sagemaker_lp) +