Example of Saving an MLFlow

saving_mlflow_model.py

from sklearn.linear_model import ElasticNet

# these are internal wrapper/utility classes that we have developed to streamline the ML lifecycle process
from hs_mllib.model_lifecycle.packaging import MLModel, ScikitLearnModel

# this context MLFlow context manager allows experiment runs (parameters and metrics) to be tracked and easily queryable
with MLModel.mlflow.start_run() as run:
    # data transformations and feature pre-processing code omitted (boiler-plate code)
    ...

    # model construction
    lr = ElasticNet(alpha=alpha, l1_ratio=l1_ratio, random_state=42)

    # training
    lr.fit(train_x, train_y)

    # evaluate the model performance
    predicted_qualities = lr.predict(test_x)
    (rmse, mae, r2) = eval_metrics(test_y, predicted_qualities)

# Wrap the model in our custom wrapper class
model = ScikitLearnModel(lr)
model.log_params(...)
model.log_metrics(...)  # record the results of the run in ML Tracking Server

# optionally save model artifacts to object store and register model (give it a semantic version)
# so it can be built into a Sagemaker-servable Docker image
model.save(register=True)