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You have deployed a machine learning model into production using Databricks and MLflow. The underlying data patterns frequently change. How can you automate the process of retraining and deploying this model to ensure its accuracy over time?
A
Periodically export model metrics using Databricks CLI and decide when to retrain the model using an external tool.
B
Manually monitor model performance metrics and trigger retraining in Databricks Notebooks as needed.
C
Set up a scheduled job in Databricks to retrain the model using the latest data, automatically logging the new model version with MLflow, and manually promote it to production.
D
Use MLflow to track model performance, and when a performance threshold is crossed, automatically trigger a Databricks pipeline to retrain and update the model in production.
