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Creating an MLproject for the ML Lifecycle: Model Engineering

The MLproject file can include more than one entry point. This means that you can use a single MLproject file to execute multiple entry points, making it possible to execute a workflow of multiple steps using a single MLproject file.

In this exercise you are going to build the beginning of an MLproject file that contains the model_engineering entry point. This entry point will execute a python script that accepts parameters used as hyperparameter values for fit_intercept and n_jobs to a Logistic Regression model. This model is used to predict sex of person from an insurance claim.

This exercise is part of the course

Introduction to MLflow

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Exercise instructions

  • Create an entry point for the Model Engineering step of the ML lifecycle called model_engineering.
  • Set the first entry point parameter to n_jobs and and second to fit_intercept.
  • Place the parameters within the command.

Hands-on interactive exercise

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

"""
name: insurance_model
python_env: python_env.yaml
entry_points:
  # Set the entry point
  ____:
    parameters: 
      # Set n_jobs 
      ____:
        type: int
        default: 1
      # Set fit_intercept
      ____:
        type: bool
        default: True
    # Pass the parameters to the command
    command: "python3.9 train_model.py {____} {____}"
"""
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