Creating a multi-step workflow: Model Evaluation
In this exercise, you will create the Model Evaluation step of our multi-step workflow used to manage part of the ML lifecycle. You will use the run()
method from the MLflow Projects module and set the entry point to model_evaluation
. You will then take the model_engineering_run_id
as a parameter that was generated as an output in the previous exercise and pass it to the command.
The MLproject
created in the previous step is available in the IPython Shell using print(MLproject)
.
The mlflow
module is imported.
This exercise is part of the course
Introduction to MLflow
Exercise instructions
- Assign the
run()
method from MLflow Projects module tomodel_evaluation
. - Set the entry point argument to
"model_evaluation"
. - Set a parameter called
"run_id"
with a value ofmodel_engineering_run_id
.
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Set the MLflow Projects run method
model_evaluation = ____.____.____(
uri="./",
# Set the entry point to model_evaluation
____="____",
# Set the parameter run_id to the run_id output of previous step
parameters={
"____": ____,
},
env_manager="local"
)
print(model_evaluation.get_status())