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Answer: To store, manage, and retrieve ML features for training and inference
Amazon SageMaker Feature Store is a purpose-built repository designed specifically for storing, managing, and retrieving machine learning features. It serves as a centralized feature store that enables data scientists and ML engineers to: 1. **Store features** - Maintain a consistent repository of features used in ML models 2. **Manage features** - Organize, version, and track features throughout their lifecycle 3. **Retrieve features** - Efficiently access features for both training and inference workflows This centralized approach ensures consistency between training and inference, reduces data duplication, and improves collaboration among data science teams. The other options describe different AWS services: - **Option A**: Data visualization is typically handled by services like Amazon QuickSight - **Option C**: GPU monitoring is part of Amazon CloudWatch or SageMaker Debugger - **Option D**: Model deployment as REST APIs is handled by Amazon SageMaker endpoints
Author: Ritesh Yadav
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What is the main function of Amazon SageMaker Feature Store?
A
To create data visualizations for ML experiments
B
To store, manage, and retrieve ML features for training and inference
C
To monitor GPU utilization in ML pipelines
D
To deploy ML models as REST APIs
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