When I reviewed Amazon SageMaker in 2018, I noted that it was a highly scalable machine learning and deep learning service that supports 11 algorithms of its own, plus any others you supply. Hyperparameter optimization was still in preview, and you needed to do your own ETL and feature engineering. 

Since then, the scope of SageMaker has expanded, augmenting the core notebooks with IDEs (SageMaker Studio) and automated machine learning (SageMaker Autopilot) and adding a bunch of important services to the overall ecosystem, as shown in the diagram below. This ecosystem supports machine learning from preparation through model building, training, and tuning to deployment and management — in other words, end to end.

amazon sagemaker 01 IDG

Amazon SageMaker Studio improves on the older SageMaker notebooks, and a number of new services have enhanced the SageMaker ecosystem to support end-to-end machine learning.

What’s new in SageMaker?

What’s new? Given that I last looked at SageMaker just after it was released, the list is rather long, but let’s start with the most visible services.