Registering the model

The final step is to register and log the fitted model using MLflow. This allows you to track and version your models for production deployment.

The datetime, mlflow, mlforecast.flavor packages, and the fitted mlf model are preloaded for you.

Cet exercice fait partie du cours

Designing Forecasting Pipelines for Production

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Instructions

Set the run_name using the current timestamp created for you in the run_time variable.
Use mlflow.start_run() to start a run with the specified experiment ID.
Log the model using the right method.

Exercice interactif pratique

Essayez cet exercice en complétant cet exemple de code.

experiment_name = "ml_forecast"
try:
    mlflow.create_experiment(name=experiment_name)
    meta = mlflow.get_experiment_by_name(experiment_name)
    print(f"Setting a new experiment {experiment_name}")
except:
    print(f"Experiment {experiment_name} exists, pulling the metadata")
    meta = mlflow.get_experiment_by_name(experiment_name)

# Setup the run name and time
run_time = datetime.datetime.now().strftime("%Y-%m-%d %H-%M-%S")
run_name = f"lightGBM6_{____}"

# Start the run
with mlflow.____(experiment_id=meta.experiment_id, run_name=run_name) as run:
    # Log the model
    mlforecast.flavor.____(model=mlf, artifact_path="prod_model")
    print(f"MLflow Run created - Name: {run_name}, ID: {run.info.run_id}")

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