Today, companies are establishing feature stores to provide a central repository to scale ML development across business units and data science teams. As feature data grows in size and complexity, data scientists need to be able to efficiently query these feature stores to extract datasets for experimentation, model training, and batch scoring.
Amazon SageMaker Feature Store is a purpose-built feature management solution that helps data scientists and ML engineers securely store, discover, and share curated data used in training and prediction workflows. SageMaker Feature Store now supports Apache Iceberg as a table format for storing features. This accelerates model development by enabling faster query performance when extracting ML training datasets, taking advantage of Iceberg table compaction. Depending on the design of your feature groups and their scale, you can experience training query performance improvements of 10x to 100x by using this new capability.
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