What technology improves privacy and reduces latency for online prediction tasks?

Endpoints serve as the correct technology for improving privacy and reducing latency in online prediction tasks. When using machine learning models for predictions, particularly in a cloud-based environment, endpoints provide a streamlined way for applications to interact with the model. By enabling a direct and secure connection, endpoints allow for real-time queries and responses, which is essential for applications requiring immediate feedback.

The reduction in latency is significant because endpoints are specifically designed for low-latency interactions. This means that once a model is deployed to an endpoint, it can process requests much faster compared to other methods that might involve additional steps or data transfers.

Moreover, endpoints can enhance privacy by providing options for secure communication channels. This is crucial when handling sensitive data, as it allows for secure interactions, ensuring that data sent to the endpoint is protected during transmission.

In contrast, the other options do not specifically focus on improving privacy or latency for online predictions. Cloud Storage is primarily used for storing data, not executing real-time predictions. BigQuery is a data analytics platform that allows for SQL-like querying of large datasets, but it isn't optimized specifically for real-time predictions. Dataflow is used for data processing and event-driven applications, which does not directly translate to predictive latency and privacy in the context of serving machine