Saving and loading a model
With the Model API, models can be shared between developers who may not have access to the same MLflow Tracking server by using a local filesystem.
In this exercise, you will train a new LinearRegression model from an existing one using the Unicorn
dataset. First, you will load an existing model from the local filesystem. Then you will train a new model from the existing model and save it back to the local filesystem.
The existing model has been saved to the local filesystem in a directory called "lr_local_v1"
. The mlflow
module will be imported.
This exercise is part of the course
Introduction to MLflow
Exercise instructions
- Load the model from the local filesystem directory
"lr_local_v1"
using scikit-learn library from the MLflow module. - Using the scikit-learn library from the
mlflow
module, save the model locally to a directory called"lr_local_v2"
.
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Load model from local filesystem
model = ____.____.____("____")
# Training Data
X = df[["R&D Spend", "Administration", "Marketing Spend", "State"]]
y = df[["Profit"]]
X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.7,random_state=0)
# Train Model
model.fit(X_train, y_train)
# Save model to local filesystem
____.____.____(____, "____")