Amazon SageMaker Feature Store now supports batch feature writes and record listing
Amazon SageMaker Feature Store is a fully managed capability that makes it easy to compute, store, and retrieve features for training and deploying AI models. SageMaker Feature Store now supports new capabilities for high-throughput feature ingestion, record discovery, and offline store cataloging. Data scientists can now write multiple records across multiple feature groups in a single request with BatchWriteRecord, list the records stored in a feature group without knowing each record identifier in advance with ListRecords, and create tables and databases with custom names in the offline sto
The continuous evolution of AI development demands more efficient and flexible methods for managing features, driving AWS to enhance its SageMaker offerings.
This update streamlines the MLOps pipeline for data scientists, significantly reducing the complexity and time involved in feature engineering and management for AI models.
Data scientists can now more easily ingest features in bulk, discover stored records without prior IDs, and customize offline storage, leading to faster model iteration and deployment.
- · AWS (Amazon)
- · Data Scientists
- · AI/ML Developers
- · Enterprises using SageMaker
- · manual feature management workflows
- · less advanced MLOps platforms
Increased efficiency and broader adoption of Amazon SageMaker for AI development.
Faster innovation cycles in AI-driven products and services leveraging SageMaker.
Potential for a more competitive landscape in cloud AI services as AWS strengthens its platform capabilities.
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Read at AWS What's New