Which of the following is NOT a feature of Databricks Machine Learning?

The choice identifying static model analytics reporting as not being a feature of Databricks Machine Learning highlights an important distinction in how Databricks focuses on modern approaches to machine learning.

In Databricks Machine Learning, the emphasis is on dynamic and interactive analytics that facilitate real-time insights and ongoing model evaluation. Instead of static reporting, the platform provides tools that allow for continuous monitoring and adaptation of models to changing data, offering enhanced flexibility and responsiveness to model performance.

The other options represent key functionalities within the Databricks ecosystem. Optimized machine learning frameworks enable efficient processing and execution of machine learning algorithms, while built-in automated machine learning development streamlines the model creation process through automation. Moreover, the capability for built-in real-time model serving allows for deploying models that can respond to incoming data and requests instantaneously, ensuring that analytics and insights are as current as possible.

In summary, the focus of Databricks on dynamic processes and real-time capabilities aligns with the need for modern machine learning practices, making static model analytics reporting an inadequate fit for their offerings.