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.

この演習はコースの一部です

Designing Forecasting Pipelines for Production

コースを見る

演習の手順

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.

実践的なインタラクティブ演習

このサンプルコードを完成させて、この演習に挑戦してみましょう。

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}")

コードを編集して実行