Amazon SageMaker Autopilot, a low-code machine learning (ML) service that automatically builds, trains, and tunes the best ML models based on tabular data, is now integrated with Amazon SageMaker Pipelines, the first purpose-built continuous integration and continuous delivery (CI/CD) service for ML. This enables the automation of an end-to-end flow of building ML models using Autopilot and integrating models into subsequent CI/CD steps.
So far, to launch an Autopilot experiment within Pipelines, you have to build a model-building workflow by writing custom integration code with Pipelines Lambda or Processing steps. For more information, see Move Amazon SageMaker Autopilot ML models from experimentation to production using Amazon SageMaker Pipelines.
With the support for Autopilot as a native step within Pipelines, you can now add an automated training step (AutoMLStep) in Pipelines and invoke an Autopilot experiment with Ensembling training mode. For example, if you’re building a training and evaluation ML workflow

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