MLCOST-08: Enable feature reusability

Use Amazon SageMaker AI Feature Store - Amazon SageMaker AI Feature Store is a fully managed, purpose-built repository to store, update, retrieve, and share ML features. Feature Store makes it easy for data scientists, machine learning engineers, and general practitioners to create, share, and manage features for ML development. The online store is used for low latency, real-time inference use cases. The offline store is used for training and batch inference. The Feature Store reduces the repetitive data processing and curation work required to convert raw data into features for training an ML algorithm.

Online - Features are read with low latency reads (milliseconds) and used for high throughput predictions. 

Offline - Large streams of data are fed to an offline store, which is used for training and batch inference. This mode requires a feature group to be stored in an offline store. The offline store uses your S3 bucket for storage and can also fetch data using Amazon Athena queries.

Online and offline - This includes both online and offline modes.

Create, Store, and Share Features with Amazon SageMaker AI Feature Store

Getting started with Amazon SageMaker AI Feature Store

Store, Discover, and Share Machine Learning Features with Amazon SageMaker AI Feature Store

Enable feature reuse across accounts and teams using Amazon SageMaker AI Feature Store

Understanding the key capabilities of Amazon SageMaker AI Feature Store

Using Amazon SageMaker AI Feature Store with streaming feature aggregation

Extend model lineage to include ML features using Amazon SageMaker AI Feature Store

Amazon SageMaker AI Feature Store Deep Dive Demo

Using Amazon SageMaker AI Feature Store with streaming feature aggregation

Amazon SageMaker AI Feature Store Notebook Examples