Maximize Feature Engineering ROI & Data Scientist Productivity Using Amazon SageMaker Feature Store

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Published Feb 20, 2025

Organizations adopting Machine Learning often struggle to keep a single, consistent source of features across teams. Data science teams are often developing models in isolation, as there traditionally have been few ways to effectively reuse features across multiple use cases and projects, leverage features with high performance inference demands, and enforce proper feature access control. Join this tech talk to learn how Amazon SageMaker Feature Store is helping machine learning engineers and data scientists accelerate the ML lifecycle by providing a unified store for features during training and real-time inference, and in turn, helping accelerate productivity and mitigating operational overhead.

Learning Objectives:
*Understand how to create, share, and discover features across teams
*Learn how use consistent prebuilt features for training and real-time inference
*Get started with managing and governing features to optimize team efficiency and traceability

***To learn more about the services featured in this talk, please visit: http:// https://aws.amazon.com/sagemaker/ Subscribe to AWS Online Tech Talks On AWS:
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