How does Databricks support machine learning?

Databricks supports machine learning through its robust integration of machine learning workflows, libraries, and lifecycle management. This comprehensive approach allows data scientists and engineers to collaboratively create, train, and deploy machine learning models within a unified environment. Databricks provides built-in libraries for machine learning, such as MLlib and integration with popular libraries like TensorFlow and PyTorch, facilitating various tasks from data preprocessing to model evaluation.

Additionally, the platform's MLflow library promotes effective tracking of experiments, versioning of models, and deployment across different environments. This end-to-end management is crucial for maintaining and scaling machine learning initiatives in production. By facilitating collaboration and offering tools that span the entire machine learning lifecycle, Databricks enhances the efficiency and effectiveness of building machine learning solutions.

In contrast to the other options, standalone models do not encompass the necessary scalability and integration of workflows throughout the model's lifecycle. Merely providing data is only one aspect of machine learning, and focusing solely on data storage overlooks the essential capabilities required for developing and deploying machine learning applications effectively.