Üretim için Tahmin (Forecasting) Hatları Tasarlama
Rami Krispin
Senior Manager, Data Science and Engineering


Veri alımı
Tahmin yenileme
Dayanıklı






Yaklaşımlar
Gereklilikler
from lightgbm import LGBMRegressor from mlforecast import MLForecast import mlflow import mlforecast.flavorexperiment_name = "ml_forecast" mlflow_path = "file:///mlruns"meta = mlflow.get_experiment_by_name(experiment_name)
model = LGBMRegressor(n_estimators = 500, learning_rate= 0.05)params = { "freq": "h", "lags": list(range(1, 24)), "date_features": ["month", "day", "dayofweek", "week", "hour"] }
mlf = MLForecast(
models= model,
freq= params["freq"],
lags=params["lags"],
date_features=params["date_features"]
)
mlf.fit(ts)
run_time = datetime.datetime.now().strftime("%Y-%m-%d %H-%M-%S") run_name = f"lightGBM6_{run_time}"print(run_name)
'lightGBM6_2025-05-19 05-12-16'
with mlflow.start_run(experiment_id=meta.experiment_id,
run_name=run_name) as run:
mlforecast.flavor.log_model(model=mlf, artifact_path="prod_model")



Üretim için Tahmin (Forecasting) Hatları Tasarlama