Loading models from the Model Registry
In this exercise, you will use the scikit-learn flavor to deploy the most stable "Insurance"
model from the MLflow Model Registry and then use test data to get a prediction from the model.
The model uses LogisticRegression to predict whether an insurance claim is for a male or female, which is labeled as 1 or 0. You'll load the model and then make predictions using a test set called X_test
.
The MLflow module will be imported.
This exercise is part of the course
Introduction to MLflow
Exercise instructions
- With the scikit-learn flavor, load the
"Production"
version of the"Insurance"
model using the convention for fetching models from the Registry as the model URI. - Using the loaded model, run a prediction on the test data from
train_test_split
used during training the model.
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Load the Production stage of Insurance model using scikit-learn flavor
model = ____.____.____("____")
# Run prediction on our test data
____.____(____)