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Creating an MLproject for the ML Lifecycle: Model Evaluation

In this exercise, you will continue creating your MLproject file to manage steps of the ML lifecycle. You will create another entry point called model_evaluation. This step in the workflow accepts the run_id output from the model_engineering step and runs model evaluation using training data from our Insurance dataset.

You can print the current MLproject file using the IPython Shell and executing print(MLproject).

This exercise is part of the course

Introduction to MLflow

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Exercise instructions

  • Create an entry point called model_evaluation.
  • Set parameters for run_id.
  • Place the parameter within the command.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

"""
  # Set the model_evaluation entry point
  ____:
    parameters:
      # Set run_id parameter
      ____:
        type: str 
        default: None
    # Set the parameters in the command
    command: "python3.9 evaluate.py {____}"
"""
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