What is the primary purpose of Databricks Machine Learning?

The primary purpose of Databricks Machine Learning is to deliver an integrated platform specifically designed for building, training, and deploying machine learning models at scale. This platform offers a collaborative environment where data scientists and engineers can work together seamlessly, leveraging powerful tools and libraries for machine learning.

Databricks incorporates features that streamline the entire machine learning workflow, from data preparation and feature engineering to model training and deployment. This integration allows users to efficiently manage their machine learning lifecycle, automate model training processes, and easily scale their applications in response to varying workloads. Moreover, the environment supports popular machine learning frameworks and libraries, making it versatile for many data science tasks.

In contrast, other options serve different purposes that are not aligned with the central focus on machine learning model development and deployment. For instance, while cloud storage solutions are essential for managing data, they do not directly involve the intricate processes around machine learning. Similarly, creating visualizations or managing data access permissions are important for data management and analysis but do not reflect the comprehensive capabilities that Databricks Machine Learning offers for the entire machine learning process.