Logging tuned models

You have been experimenting with different model hyperparameters and need to log your latest round of experiment results to MLflow, let's do it!

แบบฝึกหัดนี้เป็นส่วนหนึ่งของหลักสูตร

Designing Forecasting Pipelines for Production

ดูคอร์ส

คำแนะนำการฝึกหัด

Set the experiment name as "hyperparameter_tuning".
Loop over the index and rows of df.
Start an MLflow run.

แบบฝึกหัดเชิงโต้ตอบแบบลงมือทำ

ลองทำแบบฝึกหัดนี้โดยเติมโค้ดตัวอย่างนี้ให้สมบูรณ์

# Set the experiment name
experiment_name = "____"
experiment_id = mlflow.create_experiment(experiment_name)

# Loop through the DataFrame
for idx, row in df.____():
  # Start a run
  with mlflow.____(experiment_id=____):
    model_params = ml_models[row["model_label"]].get_params()
    model_params["model_name"] = row["model_name"]
    model_params["model_label"] = row["model_label"]
    model_params["partition"] = row["partition"]
    model_params["lags"] = list(range(1, 24))
    model_params["date_features"] = ["month", "day", "dayofweek", "week", "hour"]
    mlflow.log_params(model_params)
    mlflow.log_metric("mape", row["mape"])
    mlflow.log_metric("rmse", row["rmse"])

แก้ไขและรันโค้ด