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
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 {____}"
"""