# Next steps and resources
<a name="whats-next"></a>

This guide walks you through on a few considerations when planning the lifecycle of the machine learning models you want to bring to production. It discusses challenges and best practices in four areas—data, training, deployment, and monitoring—and includes additional relevant resources.

AWS provides the Well-Architected Framework, which helps cloud architects build secure, high-performing, resilient, and efficient infrastructures for a variety of applications, workloads, and technology domains. For additional reading, see the [Machine Learning Lens](https://docs.aws.amazon.com/wellarchitected/latest/machine-learning-lens/machine-learning-lens.html) offered by AWS Well-Architected.

## Resources
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**Amazon SageMaker AI documentation**
+ [Amazon SageMaker AI Feature Store](https://docs.aws.amazon.com/sagemaker/latest/dg/feature-store-getting-started.html)
+ [Feature Store security and access control](https://docs.aws.amazon.com/sagemaker/latest/dg/feature-store-security.html)
+ [Shapley values](https://docs.aws.amazon.com/sagemaker/latest/dg/clarify-shapley-values.html)
+ [Amazon SageMaker AI Debugger](https://docs.aws.amazon.com/sagemaker/latest/dg/train-debugger.html)
+ [Amazon SageMaker AI Pipelines](https://docs.aws.amazon.com/sagemaker/latest/dg/pipelines-sdk.html)
+ [Amazon SageMaker AI default project templates](https://docs.aws.amazon.com/sagemaker/latest/dg/sagemaker-projects-templates-sm.html)
+ [SageMaker AI real-time inference](https://docs.aws.amazon.com/sagemaker/latest/dg/realtime-endpoints.html)
+ [Automatically scale Amazon SageMaker AI models](https://docs.aws.amazon.com/sagemaker/latest/dg/endpoint-auto-scaling.html)
+ [Amazon SageMaker AI asynchronous inference](https://docs.aws.amazon.com/sagemaker/latest/dg/async-inference.html)
+ [SageMaker AI Model Monitor](https://docs.aws.amazon.com/sagemaker/latest/dg/model-monitor-data-capture.html)

**AWS developer tools**
+ [AWS CodePipeline](https://aws.amazon.com/codepipeline/)

**AWS blog posts**
+ [Understanding the key capabilities of Amazon SageMaker AI Feature Store](https://aws.amazon.com/blogs/machine-learning/understanding-the-key-capabilities-of-amazon-sagemaker-feature-store/)
+ [Testing data quality at scale with PyDeequ](https://aws.amazon.com/blogs/big-data/testing-data-quality-at-scale-with-pydeequ/)
+ [Amazon SageMaker AI Experiments](https://aws.amazon.com/blogs/aws/amazon-sagemaker-experiments-organize-track-and-compare-your-machine-learning-trainings/)
+ [Safely deploying and monitoring Amazon SageMaker endpoints with CodePipeline and AWS CodeDeploy](https://aws.amazon.com/blogs/machine-learning/safely-deploying-and-monitoring-amazon-sagemaker-endpoints-with-aws-codepipeline-and-aws-codedeploy/)
+ [Deploy shadow ML models in Amazon SageMaker AI](https://aws.amazon.com/blogs/machine-learning/deploy-shadow-ml-models-in-amazon-sagemaker/)
+ [A/B Testing ML models in production using Amazon SageMaker AI](https://aws.amazon.com/blogs/machine-learning/a-b-testing-ml-models-in-production-using-amazon-sagemaker/)