Summary
In this chapter, we gave you an overview of Azure AutoML. We talked about how featurization, which can be an extremely time-consuming task, is handled by AutoML. We then explored how to use AutoML via AMLS for a no-code experience. Finally, we walked you through writing code using AutoML via the AML Python SDK and how to view and parse the output.
In the next chapter, we will show you how to deploy your ML models for real-time inference – for example, calling a REST API exposing the trained model – and for batch scoring.
