/amazon-sagemaker-feature-store-end-to-end-workshop

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SageMaker Feature Store Workshop

workshop

  • 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.