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  3. Designing Forecasting Pipelines for Production
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Exercise

Registering the model

The final step is to register and log the fitted model using MLflow. This allows you to track and version your models for production deployment.

The datetime, mlflow, mlforecast.flavor packages, and the fitted mlf model are preloaded for you.

Instructions

100 XP
  • Set the run_name using the current timestamp created for you in the run_time variable.
  • Use mlflow.start_run() to start a run with the specified experiment ID.
  • Log the model using the right method.