Announcing Databricks Autologging for Automated ML Experiment Tracking
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...
Introducing Databricks AutoML: A Glass Box Approach to Automating Machine Learning Development
Today, we announced Databricks AutoML, a tool that empowers data teams to quickly build and deploy machine learning models by automating the heavy lifting of preprocessing, feature engineering and model training/tuning. With this launch, data teams can select a dataset, configure training, and deploy models entirely through a UI. We also provide an advanced experience...
Introducing the Databricks Web Terminal
Introduction We're excited to introduce the public preview of the Databricks Web Terminal in the 3.25 platform release. Any user with "Can Attach To" cluster permissions can now use the Web Terminal to interactively run Bash commands on the driver node of their cluster. The new Databricks web terminal provides a fully interactive shell that...
