AWS Expands Amazon SageMaker with No-Code Tools and Managed Data Labeling
AWS announces new Amazon SageMaker features at re:Invent 2021 that bring machine learning to business analysts and simplify data labeling. The updates also unify data processing and model building within a single notebook interface.
AWS introduces several new Amazon SageMaker features at re:Invent 2021 that make machine learning accessible to a broader audience. Amazon SageMaker Canvas now offers business analysts a visual point-and-click interface to generate accurate ML predictions without writing any code. This expansion allows professionals in finance, marketing, operations, and HR to leverage ML directly in their daily workflows.
To address the growing demand for high-quality training data, AWS launches Amazon SageMaker Ground Truth Plus. This fully managed service provides an expert workforce trained on ML tasks to handle data labeling workflows on behalf of customers. Users simply upload their data, and the service takes care of the rest while ensuring strict data security, privacy, and compliance requirements are met.
AWS also enhances the developer experience by unifying data processing, analytics, and ML workflows inside a single Amazon SageMaker Studio notebook. This universal notebook allows data scientists to connect to various data sources and write transformation code for diverse workloads in one place. These updates collectively aim to optimize both the performance and cost of building, training, and deploying machine learning models.