What is a pipeline in the context of Databricks ML?

A pipeline in the context of Databricks ML refers to a sequence of data processing steps that help automate and streamline the workflow of machine learning tasks. This concept encompasses the entire process of data preparation, model training, and model evaluation, integrating various stages into a cohesive workflow. By structuring the workflow as a pipeline, it becomes easier to manage and reproduce the machine learning tasks, ensuring that each step is executed in order and with the right dependencies.

This structured approach allows data scientists and engineers to organize their projects effectively, facilitating better collaboration and version control. Pipelines also promote consistency and efficiency by automating repetitive tasks, which enhances the overall productivity of the team involved in the machine learning project. Whether it's feature engineering, model training, or deployment, pipelines ensure that the correct sequence of operations is maintained, leading to more reliable and robust models.

The other options address aspects of machine learning but do not capture the essence of what a pipeline is in this context. User feedback collection, model evaluation techniques, and real-time data streaming represent important components of the broader machine learning ecosystem, but they do not directly describe the structured workflow that a pipeline facilitates.