What is a key feature of the Databricks Runtime for Machine Learning compared to non-ML runtimes?

A key feature of the Databricks Runtime for Machine Learning is that it includes built-in deep learning libraries. This feature significantly enhances the capability of users to build and deploy machine learning models that involve complex neural networks. The inclusion of these libraries, such as TensorFlow, PyTorch, and Keras, allows data scientists and machine learning engineers to easily integrate state-of-the-art deep learning techniques into their workflows without the need for extensive setup or configuration.

Furthermore, built-in libraries ensure that the environment is optimized for performance and compatibility, simplifying the process of developing and experimenting with deep learning models. By providing these tools out of the box, Databricks minimizes the overhead associated with setting up deep learning frameworks, allowing users to focus more on their projects and less on environment management.

This focus on deep learning libraries distinguishes the Databricks Runtime for Machine Learning from non-ML runtimes, which may lack those specialized tools and optimizations that support advanced machine learning tasks.