Building an ML model involves substantial trial and error and iterating on hundreds to thousands of models. Each of these experiments in model building are essential to the development of the final model and for analyzing and debugging the performance of different models.
Version each model built in a project and manage the experiments via intuitive dashboards, reporting, and model analysis functionality:
Flexible metadata logging including metrics, artifacts, tags, and user information
ML experiment management
Model governance and management
Beautiful dashboards for model performance and reporting