Amazon Web Services claims to have the broadest and most complete set of machine learning capabilities. I honestly don’t know how the company can claim those superlatives with a straight face: Yes, the AWS machine learning offerings are broad and fairly complete and rather impressive, but so are those of Google Cloud and Microsoft Azure.
Amazon SageMaker Clarify is the new add-on to the Amazon SageMaker machine learning ecosystem for Responsible AI. SageMaker Clarify integrates with SageMaker at three points: in the new Data Wrangler to detect data biases at import time, such as imbalanced classes in the training set, in the Experiments tab of SageMaker Studio to detect biases in the model after training and to explain the importance of features, and in the SageMaker Model Monitor, to detect bias shifts in a deployed model over time.
Historically, AWS has presented its services as cloud-only. That is starting to change, at least for big enterprises that can afford to buy racks of proprietary appliances such as AWS Outposts. It’s also changing in AWS’s industrial offerings, such as Amazon Monitron and AWS Panorama, which include some edge devices.
IDG
This diagram summarizes the AWS Machine Learning stack as of December 2020. It appeared often during talks at AWS re:Invent.