Which methods does the feature store offer for serving features?

The feature store provides capabilities for different methods of serving features that are crucial for various machine learning (ML) use cases. Online serving refers to the ability to retrieve features in real-time, making it suitable for applications that require immediate predictions such as user recommendations or fraud detection. Batch serving, on the other hand, allows the retrieval of features for a whole dataset at once, which is ideal for offline processing tasks like retraining models or generating reports.

Selecting batch and online serving captures the practical needs of machine learning algorithms without neglecting the importance of latency and throughput. Online serving enables low-latency access to features essential for real-time applications, while batch serving is more efficient for processing large amounts of data at once, ensuring that the feature store supports diverse operational requirements across different models and environments.

This combination makes feature stores particularly powerful, as they can support both low-latency real-time predictions and high-throughput batch processing in a scalable manner. The other options do not accurately reflect the capabilities of serving features in a feature store context, as they either misrepresent the serving concepts or combine terms that do not commonly align with ML best practices.