Summary

In this chapter, we used SageMaker Pipelines to build end-to-end automated ML pipelines. We started by preparing a relatively simple pipeline with three steps—including the data preparation step, the model training step, and the model registration step. After preparing and defining the pipeline, we proceeded with triggering a pipeline execution that registered a newly trained model to the SageMaker Model Registry after the pipeline execution finished running.

Then, we prepared three AWS Lambda functions that would be used for the model deployment steps of the second ML pipeline. After preparing the Lambda functions, we proceeded with completing the end-to-end ML pipeline by adding a few additional steps to deploy the model to a new or existing ML inference endpoint. Finally, we discussed relevant best practices and strategies to secure, scale, and manage ML pipelines using the technology stack we used in this chapter.

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