📊Tracking: AWS SageMaker

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### Overview:

Amazon SageMaker is a fully managed machine learning service. With Amazon SageMaker, data scientists and developers can quickly and easily build and train machine learning models, and then directly deploy them into a production-ready hosted environment. It provides an integrated Jupyter authoring notebook instance for easy access to your data sources for exploration and analysis, so you don't have to manage servers. It also provides common machine learning algorithms that are optimized to run efficiently against extremely large data in a distributed environment. With native support for bring-your-own-algorithms and frameworks, Amazon SageMaker offers flexible distributed training options that adjust to your specific workflows. Deploy a model into a secure and scalable environment by launching it with a single click from the Amazon SageMaker console. Training and hosting are billed by minutes of usage, with no minimum fees and no upfront commitments.

[AWS Docs](https://docs.aws.amazon.com/sagemaker/latest/dg/whatis.html)

### Maturity: CloudFormation Resources Only

See the [AWS Construct Library Module Lifecycle doc](https://github.com/aws/aws-cdk-rfcs/blob/master/text/0107-construct-library-module-lifecycle.md) for more information about maturity levels.

### Implementation:

See the [CDK API Reference](https://docs.aws.amazon.com/cdk/api/latest/docs/aws-sagemaker-readme.html) for more implementation details.

### Issue list:

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This is a 📊Tracking Issue

Start with the linked AWS Docs and CDK API Reference to understand the SageMaker service and existing CloudFormation resources. No implementation files, tests, or concrete construct scope are named in this tracking issue. Done would require an agreed high-level construct plan and the corresponding implementation and validation scope.

Written by the indexing model from the issue text.