Module 1: Feature Store Foundations

• Topics:
  • Dataset introduction
  • Creating a feature group
  • Ingesting a Pandas DataFrame into Online/Offline feature store
  • GetRecord, ListFeatureGroups, DescribeFeatureGroup

Module 2: Working with the Offline Store

• Topics:
  • Look at data in S3 console (Offline feature store)
  • Athena query for dataset extraction (via Athena console)
  • Athena query for dataset extraction (programmatically using SageMaker SDK)
  • Extract a training dataset and storing in S3

Module 3: Training a model using extracted dataset from the Offline feature store

• Topics:
  • Training a model using feature sets derived from the Offline feature store
  • Deploying the trained model for real-time inference

Module 4: Leveraging the Online feature store

• Topics:
  • Get record from Online feature store during single inference
  • Get multiple records from Online store using BatchGet during batch inference

Module 5: Scalable batch ingestion using distributed processing

• Topics:
  • Batch ingestion via SageMaker Processing job
  • Batch ingestion via SageMaker Processing PySpark job
  • SageMaker Data Wrangler export job to feature store

Module 6: Automate feature engineering pipelines with Amazon SageMaker

• Topics:
  • Leverage Amazon SageMaker Data Wrangler, Amazon SageMaker Feature Store, and Amazon SageMaker Pipelines alongside AWS Lambda to automate feature transformation.