Which AWS service is used to store, share and manage inputs to Machine Learning models used during training and inference?

Which AWS service is used to store, share and manage inputs to Machine Learning models used during training and inference?
A . Amazon SageMaker Ground Truth
B . Amazon SageMaker Feature Store
C . Amazon SageMaker Clarify
D . Amazon SageMaker Data Wrangler

View Answer

Answer: B

Explanation:

Correct option:

Amazon SageMaker Feature Store

Amazon SageMaker Feature Store is a fully managed, purpose-built repository to store, share, and manage features for machine learning (ML) models. Features are inputs to ML models used during training and inference. For example, in an application that recommends a music playlist, features could include song ratings, listening duration, and listener demographics.

You can ingest data into SageMaker Feature Store from a variety of sources, such as application and service logs, clickstreams, sensors, and tabular data from Amazon Simple Storage Service (Amazon S3), Amazon Redshift, AWS Lake Formation, Snowflake, and Databricks Delta Lake.

How Feature Store works:

via – https://aws.amazon.com/sagemaker/feature-store/

Incorrect options:

Amazon SageMaker Clarify – SageMaker Clarify helps identify potential bias during data preparation without writing code. You specify input features, such as gender or age, and SageMaker Clarify runs an analysis job to detect potential bias in those features.

Amazon SageMaker Data Wrangler – Amazon SageMaker Data Wrangler reduces the time it takes to aggregate and prepare tabular and image data for ML from weeks to minutes. With SageMaker Data Wrangler, you can simplify the process of data preparation and feature engineering, and complete each step of the data preparation workflow (including data selection, cleansing, exploration, visualization, and processing at scale) from a single visual interface.

Amazon SageMaker Ground Truth – Amazon SageMaker Ground Truth offers the most comprehensive set of human-in-the-loop capabilities, allowing you to harness the power of human feedback across the ML lifecycle to improve the accuracy and relevancy of models. You can complete

a variety of human-in-the-loop tasks with SageMaker Ground Truth, from data generation and annotation to model review, customization, and evaluation, either through a self-service or an AWS-managed offering.

References:

https://aws.amazon.com/sagemaker/feature-store/

https://aws.amazon.com/sagemaker/groundtruth

https://aws.amazon.com/sagemaker/clarify/

https://aws.amazon.com/sagemaker/data-wrangler/