Amazon SageMaker Feature Store
Managed feature store for machine learning on AWS
Amazon SageMaker Feature Store is profiled here as a Backend tool for engineering teams. Read about features, pricing, and how it compares to related options in the tools directory.
Description
Amazon SageMaker Feature Store is a managed feature store from AWS that gives machine learning teams a central place to create, store, and serve features for training and inference. It maintains an online store for low-latency lookups during real-time prediction and an offline store in Amazon S3 for building training datasets, and it keeps the two in sync. Point-in-time queries reconstruct feature values as they existed at a past moment, which prevents data leakage when assembling training data, and the store integrates with the wider SageMaker platform.
Key Capabilities:
An online store for low-latency feature lookups during inference
An offline store in Amazon S3 for assembling training datasets
Point-in-time queries that prevent leakage in training data
Feature groups that organize and version related features
Integration with SageMaker pipelines and the AWS ecosystem
Access control and encryption through AWS identity and KMS
Alternative tools
- Anomalo
Automated data quality monitoring with machine learning
- Apache Druid
Real-time analytics database for sub-second queries
- RudderStack
Warehouse-native customer data pipeline and Segment alternative
- Storj
Distributed S3-compatible storage across a global network
- Wasabi
S3-compatible hot cloud storage without egress fees
- Better Auth
Framework-agnostic authentication library for TypeScript
