AWS Expands Amazon SageMaker to Bring Machine Learning to All Users
AWS introduces new Amazon SageMaker features from re:Invent 2021 that make machine learning accessible to business analysts while improving performance and reducing costs for experts.
AWS announces several new Amazon SageMaker features at re:Invent 2021 that make machine learning accessible to a broader audience while simultaneously boosting performance and cutting costs for data scientists. These updates aim to extend the reach of ML beyond traditional developers to include line-of-business analysts across finance, marketing, operations, and HR teams who previously lacked coding experience.
To empower these non-technical users, AWS introduces Amazon SageMaker Canvas, a visual point-and-click interface that allows business analysts to generate accurate ML predictions without writing any code. For organizations struggling to keep up with the demand for labeled training data, AWS releases Amazon SageMaker Ground Truth Plus, a managed service that provides an expert workforce to handle data labeling workflows, security, and compliance requirements on your behalf.
For data scientists and developers, AWS enhances Amazon SageMaker Studio by introducing a unified notebook that combines data processing, analytics, and ML workflows into a single interface. This universal notebook allows users to access a wide variety of data sources and write transformation code for diverse workloads, ultimately making it easier and more cost-effective to build, train, and deploy machine learning models at scale.