What is the main advantage of using Databricks notebooks for ML projects?

The primary advantage of using Databricks notebooks for machine learning projects lies in their capability for interactive data exploration and seamless code sharing among team members. Databricks notebooks facilitate a collaborative workflow, allowing multiple users to work on the same project simultaneously. They enable real-time collaboration, where changes can be viewed and edited by all team members, thus enhancing communication and speeding up the development process.

Additionally, the interactive environment allows data scientists and analysts to visualize data and results dynamically. This interactivity helps in quickly iterating on experiments and hypotheses, making it easier to refine models and approaches based on immediate feedback and insights derived from the data. This aspect is particularly beneficial in ML, where understanding data and its patterns is crucial for building accurate models.

The other options, while they may have their own merits, do not encapsulate the core benefit of Databricks notebooks as well as the collaborative, iterative nature of data exploration and code sharing.