databricks-mlflow-evaluation

Automate MLflow GenAI evaluation workflows in Databricks by using scripts that handle dataset preparation, model evaluation, and result analysis. You can run the process end-to-end with a single command specifying your dataset and prediction function.

Yes, custom scorers can be created and integrated for GenAI model evaluation in Databricks. This allows you to define specific scoring logic tailored to your evaluation needs alongside the standard automated evaluation workflow.

MLflow evaluation supports large-scale GenAI datasets in Databricks by leveraging the Databricks environment for efficient data handling and computation. This integration ensures scalable workflows for extensive model testing.

The best way to run end-to-end MLflow GenAI evaluation is using an automated skill that processes data preparation, model evaluation, and result analysis. Execute it by passing your dataset and prediction function as command arguments.

Yes, you need the mlflow[databricks] package installed to run these GenAI evaluations. This dependency provides the necessary integration to connect the evaluation scripts with the Databricks environment.

Preparing datasets before running automated MLflow GenAI evaluation ensures the data is correctly formatted for the evaluation scripts. Proper data preparation is the first step in the workflow, enabling accurate model evaluation and result analysis.