Revolutionizing ML-Ready Datasets: Amazon SageMaker Feature Store Eliminates SQL Queries for Data Scientists

Revolutionizing ML-Ready Datasets: Amazon SageMaker Feature Store Eliminates SQL Queries for Data Scientists

Revolutionizing ML-Ready Datasets: Amazon SageMaker Feature Store Eliminates SQL Queries for Data Scientists

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There’s no denying the increasing importance of machine learning (ML) in modern businesses. With the growing demand for data-based decision making, companies must optimize the data preparation process for their data science teams. Amazon Web Services (AWS) stepped up by introducing the Amazon SageMaker Feature Store, incorporating both online and offline stores to help data scientists build ML-ready datasets more efficiently.

Data scientists often struggle with accessing and managing the necessary datasets, exacerbated by cumbersome SQL querying procedures. Amazon has addressed this bottleneck by releasing the latest SageMaker Python SDK, which enables data scientists to create and manage datasets without writing SQL queries.

Solution Overview

To demonstrate the power of the Amazon SageMaker Feature Store, we will use two sample datasets – Leads and Web Marketing Metrics:

  1. Leads: Contains information on prospective customers, identified using the LeadProspectID. The LeadSource feature is updated over time, creating new records represented by LeadEventTime.

  2. Web Marketing Metrics: Engagement metrics for a lead, identified with WebProspectID. Each lead has only one record, where WebEventTime represents the time the record was created.

With the help of the “sagemaker-feature-store-offline-sdk.ipynb” notebook, we’ll showcase how to:

  • Create a dataset from a feature group
  • Join multiple feature groups
  • Perform a point-in-time join between a feature group and dataset based on timestamps
  • Retrieve feature history within a specific time range
  • Extract features as of a specific timestamp

Prerequisites: AWS account and SageMaker Jupyter notebook instance.

Section 1: Setting up the Environment

First, import required libraries and create a SageMaker session. Next, define the S3 bucket and prefix for expedited access to later.

Section 2: Create and Ingest Data into the Feature Store

Initialize the feature store and define the schema for Leads and Web Marketing Metrics feature groups. Ingest the sample data into their respective feature groups.

Section 3: Querying the Offline Store

Access the offline store’s data using the Pandas library, assuring seamless integration with data science workflows.

Section 4: The Power of SageMaker Python SDK

The SageMaker Python SDK revolutionizes working with datasets by eliminating the need for SQL queries. Features include:

  • Time Travel: Retrieve feature history within a specified time range or as of a particular timestamp.
  • Filtering Duplicate Records: Remove redundancy for cleaner datasets
  • Joining Multiple Feature Groups: Perform point-in-time accurate data merges with ease.

Section 5: Cleanup

To prevent unnecessary storage fees, delete feature groups and created datasets after use.

As machine learning continues to revolutionize industries worldwide, Amazon SageMaker Feature Store simplifies ML-ready dataset creation with its intuitive Python SDK. Data scientists can now access the necessary datasets without the hassle of SQL queries, ensuring a seamless and efficient development process for advanced ML applications.

Casey Jones Avatar
Casey Jones
12 months ago

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