Summary

This chapter introduced you to some of AWS's AI and ML capabilities, specifically Amazon SageMaker's. You saw how to interact with the service via the SageMaker Studio UI and the SageMaker SDK. Using hands-on examples, you learned how Autopilot's implementation of the AutoML methodology addresses not only the two challenges imposed by the typical ML process but also the overall criteria for automation. Particularly, how using Autopilot ensures that the ML process is reliable and streamlined. The only task required to be done by the ML practitioner is to upload the raw data to Amazon S3.

This chapter also highlights an important aspect of the AutoML methodology. While the AutoML process is repeatable in the sense that it will always produce an optimized model, once you have the model in production, there is no real need to recreate it, unless, of course, the business use case changes. Nevertheless, Autopilot creates a solid foundation to help an ML practitioner...