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.

이 연습은 강의의 일부입니다

Designing Forecasting Pipelines for Production

강의 보기

연습 안내

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.

실습형 인터랙티브 연습

이 예제를 이 샘플 코드를 완성하여 풀어보세요.

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}")

코드 편집 및 실행