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.
Questo esercizio fa parte del corso
Designing Forecasting Pipelines for Production
Istruzioni dell'esercizio
- Set the
run_nameusing the current timestamp created for you in therun_timevariable. - Use
mlflow.start_run()to start a run with the specified experiment ID. - Log the model using the right method.
Esercizio pratico interattivo
Prova a risolvere questo esercizio completando il codice di esempio.
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}")