Amazon today launched SageMaker Data Wrangler, a new AWS service designed to speed up data prep for machine learning and AI applications. Alongside it, the company took the wraps off of SageMaker Feature Store, a purpose-built product for naming, organizing, finding, and sharing features, or the individual independent variables that act like a input in a machine learning system. Beyond this, Amazon unveiled SageMaker Pipelines, which CEO Andy Jassy described as a CI/CD service for AI. And the company detailed DevOps Guru, QuickSight, and QuickSight Q, offerings that uses machine learning to identify operational issues, provide business intelligence, and find answers to questions in knowledge stores.
During a keynote at Amazon’s re:Invent conference, Jassy said that Data Wrangler has over 300 built-in conversion transformation types. The service recommends transformations based on data in a target dataset and applies these transformations to features, providing a preview of the transformations in real time. Dat Wrangler also checks to ensure that the data is “valid and balanced.” As for SageMaker Feature Store, Jassy said that the service, which is accessible from SageMaker Studio, acts as a storage component for features and can access features in either batches or subsets. SageMaker Pipelines, meanwhile, allows users to define, share, and reuse each step of an end-to-end machine learning workflow with preconfigured customizable workflow templates while logging each step in SageMaker Experiments.
Amazon DevOps Guru is a different beast altogether. It can identify missing or misconfigured alarms to warn of approaching resource limits and code and config changes that might cause outages. In addition, DevOps Guru can spotlight things like under-provisioned compute capacity, database I/O overutilization, and memory leaks while recommending remediating actions.
Amazon QuickSight aims to provide scalable, embeddable business intelligence solutions built for the cloud. To that end, Amazon says it can scale to tens of thousands of users without any infrastructure management or capacity planning. QuickSight can be embedded into applications with dashboards and is available with pay-per-session pricing, automatically generating summaries of dashboards in plain language. A complimentary AWS service called QuickSight Q answers questions in natural language, drawing on available business intelligence resources. Using natural language processing, it understands domain-specific business language, automatically generating responses that reflect industry jargon.