Customizing and reusing models generated by Amazon SageMaker Autopilot

Amazon SageMaker Autopilot automatically trains and tunes the best machine learning (ML) models for classification or regression problems while allowing you to maintain full control and visibility. This not only allows data analysts, developers, and data scientists to train, tune, and deploy models with little to no code, but you can also review a generated notebook that outlines all the steps that Autopilot took to generate the model. In some cases, you might also want to customize pipelines generated by Autopilot with your own custom components. This post shows you how to create and use models with Autopilot in a couple of clicks, then outlines how to adapt the SageMaker Autopilot generated code with your own feature selectors and custom transformers to add domain-specific features. We also use the dry run capability of Autopilot, in which Autopilot only generates code for data preprocessors, algorithms, and algorithm parameter settings.