Ultimate access to all questions.
Upgrade Now 🚀
Sign in to unlock AI tutor
How can you automate the process of continuous training and evaluation of machine learning models on Databricks using MLflow to ensure models are retrained and evaluated weekly?
A
Use Azure DevOps to trigger weekly runs of your Databricks MLflow experiments via the Databricks REST API.
B
Schedule a Databricks job to run a notebook that uses MLflow for tracking experiments, ensuring it triggers retraining scripts weekly.
C
Manually run MLflow experiments at the end of each week and compare metrics to decide on retraining.
D
Implement a listener in Azure Event Hubs that detects when a week has passed and triggers the MLflow experiment via Databricks notebooks.
