Learn how to leverage Amazon SageMaker Feature Store to efficiently store and access features for offline Model Training and online inference while enabling data science teams to track feature history.

Question

A music streaming company is building a pipeline to extract features. The company wants to store the features for Offline Model Training and online inference. The company wants to track feature history and to give the company’s data science teams access to the features.

Which solution will meet these requirements with the MOST operational efficiency?

A. Use Amazon SageMaker Feature Store to store features for model training and inference. Create an online store for online inference. Create an offline store for model training. Create an IAM role for data scientists to access and search through feature groups.
B. Use Amazon SageMaker Feature Store to store features for model training and inference. Create an online store for both online inference and model training. Create an IAM role for data scientists to access and search through feature groups.
C. Create one Amazon S3 bucket to store online inference features. Create a second S3 bucket to store offline model training features. Turn on versioning for the S3 buckets and use tags to specify which tags are for online inference features and which are for offline model training features. Use Amazon Athena to query the S3 bucket for online inference. Connect the S3 bucket for offline model training to a SageMaker training job. Create an IAM policy that allows data scientists to access both buckets.
D. Create two separate Amazon DynamoDB tables to store online inference features and offline model training features. Use time-based versioning on both tables. Query the DynamoDB table for online inference. Move the data from DynamoDB to Amazon S3 when a new SageMaker training job is launched. Create an IAM policy that allows data scientists to access both tables.

Answer

A. Use Amazon SageMaker Feature Store to store features for model training and inference. Create an online store for online inference. Create an offline store for model training. Create an IAM role for data scientists to access and search through feature groups.

Explanation

A. Using Amazon SageMaker Feature Store is the most operationally efficient solution for storing and accessing features for offline model training and online inference. By creating an online store for online inference and an offline store for model training, the company can optimize performance for each use case.

The online store provides low-latency access to features for real-time inference, while the offline store allows for batch processing and historical analysis. Creating an IAM role for data scientists grants them access to search and retrieve features from the feature groups, enabling collaboration and efficient feature management.

SageMaker Feature Store automatically tracks feature history and versions, eliminating the need for manual versioning. This integrated solution simplifies the feature engineering pipeline, reduces operational overhead, and promotes best practices for feature storage and access in a machine learning workflow.

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