What does the built-in model serving feature in Databricks provide?

The built-in model serving feature in Databricks offers real-time model serving, meaning it enables users to deploy their machine learning models in a way that allows them to make predictions in response to incoming data requests almost immediately. This feature is particularly important for applications requiring rapid responses, such as recommendations, fraud detection, or any situation where decision-making needs to happen promptly based on the most current data.

This feature streamlines the process of making models available for use without the need for extensive infrastructure management. Users can operate their models seamlessly within the Databricks environment, which enhances productivity and reduces operational overhead.

In contrast, real-time data analytics focuses on analyzing data as it is created or updated, but does not specifically relate to the deployment of models for generating predictions. Support for distributed model training refers to the capability of training models across multiple nodes or machines to improve efficiency and handling larger datasets, but it does not address the deployment stage. Automated deployment of machine learning models is a crucial aspect of model lifecycle management but is not the primary function provided by the built-in serving features in the Databricks platform, which specifically emphasize real-time interactions.