This guide shows how to create and log a Prophet forecasting model using Snowflake ML.
Snowflake also lets you log models beyond the built-in types like Prophet model , as long as they're serializable and extend the CustomModel class from snowflake.ml.model.
You can get more information here
The Prophet model is a time series forecasting tool developed by Facebook, designed to handle seasonality, holidays, and trend changes in data. It's especially useful for business time series (like sales or traffic) and is robust to missing data and outliers.
Step 1. In Snowsight, create a SQL Worksheet and open setup.sql to execute all statements in order from top to bottom.
Step 2. Download the SNF_PROPHET_FORECAST_MODEL.ipynb and PROPHET_PREDICTION.ipynb
Step 3. In Snowsight , switch to the FR_SCIENTIST role and import the notebook file downloaded in step 2. Use Database as ML_MODELS , schema as DS and warehouse as ML_FS_WH
pickle and upload it to a Snowflake stage.## Uplad the model into stage
session.file.put("ProphetModel.pkl", f"@{stage_nm}/", auto_compress=False)
# Initialize ModelContext with keyword arguments
# my_model can be any supported model type
# my_file_path is a local pickle file path
mc = custom_model.ModelContext(
artifacts={
'config': 'ProphetModel.pkl'
}
)
# Define a custom model class that utilizes the context
class MyProphetModel(custom_model.CustomModel):
def __init__(self,context:custom_model.ModelContext) -> None:
super().__init__(context)
## use 'file_path to load the piecked object
with open(self.context['config'],'rb') as f:
self.model =pickle.load(f)
@custom_model.inference_api
def predict(self,X:pd.DataFrame) -> pd.DataFrame:
X_copy = X.copy()
X_copy['ds']=pd.to_datetime(X_copy['ds'])# ensure correrct datetime
forecast = self.model.predict(X_copy)
res_df = forecast[["ds", "yhat", "yhat_lower", "yhat_upper"]]
return res_df
custom_mv = reg.log_model(
forecast_model,
model_name="Prophet_forcast_model",
version_name="v1",
conda_dependencies=["prophet"],
sample_input_data= df_1,
options={'relax_version': False},
comment = 'My Prophet forcast experiment using the CustomModel API'
)
reg = Registry(session,database_name = db,schema_name= schema)
model_name='PROPHET_FORCAST_MODEL'
version = 'V1'
mv = reg.get_model('PROPHET_FORCAST_MODEL').version('VERSION_2')
predicted_sales = mv.run(forecast_dates)
Congratulations! You've successfully built a Prophet forecasting model in a Snowflake Notebook and logged the trained model to the Snowflake ML registry, making it ready for inference and future use.