What does Databricks provide to support machine learning workloads?

Databricks provides distributed model training on big data, which is essential for efficiently handling large datasets and complex machine learning tasks. The platform is built on Apache Spark and is designed to leverage its distributed computing capabilities, allowing for parallel processing across multiple nodes. This means that machine learning models can be trained faster and more effectively because resources can be utilized to process large volumes of data simultaneously.

Additionally, Databricks supports a range of machine learning libraries and frameworks like MLlib and TensorFlow, which are optimized for performance in a distributed environment. This capability enables data scientists and engineers to build, train, and tune models that are scalable and can handle the large datasets commonly encountered in real-world applications.

In contrast, the other options do not capture the primary strength of Databricks in the context of machine learning. The platform does not specifically offer unlimited storage options; instead, it can integrate with various cloud storage solutions. While Databricks does provide data preprocessing tools, they are typically automated and designed for a more streamlined workflow rather than being purely manual. Lastly, static model deployment does not leverage the dynamic capabilities of Databricks for real-time model updates and scaling, thus missing a key aspect of machine learning in production environments.