Announcing Databricks Autologging for Automated ML Experiment Tracking

1 · Michael Xu · Aug. 27, 2021, 4:04 p.m.
Machine learning teams require the ability to reproduce and explain their results–whether for regulatory, debugging or other purposes. This means every production model must have a record of its lineage and performance characteristics. While some ML practitioners diligently version their source code, hyperparameters and performance metrics, others find it cumbersome or distracting from their rapid... The post Announcing Databricks Autologging for Automated ML Experiment Tracking appeared first o...