How do you create a cluster with the Databricks Runtime for Machine Learning?

The process of creating a cluster specifically suited for Machine Learning in Databricks involves selecting the appropriate Databricks Runtime Version that includes Machine Learning libraries and configurations. The Databricks Runtime for Machine Learning is pre-configured to support various machine learning frameworks, such as TensorFlow and PyTorch, and provides additional libraries essential for data science tasks.

By navigating to the Compute section and choosing to create a cluster, you engage with the Databricks Runtime Version setting. Selecting the version described as ML ensures that your cluster is optimized for machine learning workloads. This setting not only enhances performance but also simplifies the integration of ML libraries, resulting in a streamlined development process for ML applications.

Other options may focus on different settings or selections that do not specifically target the Databricks Runtime Version designated for machine learning, hence lacking the precision needed for creating a tailored environment for machine learning tasks.