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Prepare ML data faster and at scale with Amazon SageMaker

Data preparation for ML can be challenging because it requires extracting, normalizing data and performing feature engineering which can be time-consuming. This session covers how you can simplify the process of data preparation, feature engineering, and complete each step of the data preparation workflow, including data selection, cleansing, exploration, bias detection, and visualization, from a single visual interface with Amazon SageMaker Data Wrangler. Find out how to reduce the time it takes to aggregate and prepare data for ML from weeks to minutes. We also share how simplify data preparation at multiple stages in Retrieval Augmented Generation (RAG) models with Amazon SageMaker Data Wrangler.

Speaker: Ben Friebe, Senior ISV Solutions Architect, AWS