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.

Deze oefening maakt deel uit van de cursus

Designing Forecasting Pipelines for Production

Cursus bekijken

Oefeninstructies

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.

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

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}")

Code bewerken en uitvoeren