Summary

In this chapter, we discussed the benefits and use cases of Amazon Redshift ML BYOM for local and remote inference. We created two SageMaker models and then imported them into Redshift ML as local inference and remote inference model types. We loaded test datasets in Redshift and then we ran the prediction functions and validated both types. This demonstrates how Redshift simplifies and empowers the business community to perform inference on new data using models created outside. This method speeds up the delivery of machine learning models created outside of Redshift to the data warehouse team.

In the next chapter, you are going to learn about Amazon Forecast, which enables you to perform forecasting using Redshift ML.