Review: Amazon SageMaker plays catch-up

When I reviewed Amazon SageMaker in 2018, I famous that it was a really scalable machine learning and deep learning service that supports 11 algorithms of its very own, moreover any some others you provide. Hyperparameter optimization was continue to in preview, and you needed to do your very own ETL and element engineering. 

Since then, the scope of SageMaker has expanded, augmenting the main notebooks with IDEs (SageMaker Studio) and automated machine learning (SageMaker Autopilot) and adding a bunch of essential solutions to the in general ecosystem, as demonstrated in the diagram down below. This ecosystem supports machine learning from preparing by model building, teaching, and tuning to deployment and management — in other text, conclude to conclude.

amazon sagemaker 01 IDG

Amazon SageMaker Studio improves on the older SageMaker notebooks, and a quantity of new solutions have improved the SageMaker ecosystem to help conclude-to-conclude machine learning.

What is new in SageMaker?

What is new? Provided that I very last seemed at SageMaker just right after it was unveiled, the listing is alternatively lengthy, but let’s begin with the most noticeable solutions.