Getting Started with Prophet Model using Snowflake ML

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Getting Started with Prophet Model using Snowflake ML

Ranjeeta Pegu

Overview

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

What is Prophet

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.

Prerequisites

Access to snowflake account with ACCOUNTADMIN role

What You’ll Learn

How to build a time series forecasting model using Facebook Prophet
How to wrap the model using Snowflake’s CustomModel class
How to log and register the model in Snowflake ML
How to run predictions using the logged model directly in Snowflake

What You’ll Build

A Prophet model for time series forecasting, developed in a Snowflake Notebook
Model training and inference running directly in a Snowflake warehouse
The model logged and registered in the Snowflake ML registry for future use

Setup

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

Run Notebook

Set up environment and import libraries
After importing the necessary libraries and setting up the environment, create synthetic data.
Train the model on synthetic data
Train the Prophet model using the synthetic dataset.
Pickle the trained model and upload it to the stage
Serialize the trained model using pickle and upload it to a Snowflake stage.


## Uplad the model into stage
session.file.put("ProphetModel.pkl", f"@{stage_nm}/", auto_compress=False)

Create the custom model

# 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

Model registry


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'
)

Inference Notebook use the PROHPET_PREDICTION notebook

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)

Conclusion And Resources

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.

What You Learned

How to build a time series forecasting model using Facebook Prophet
How to develop and run the model directly in a Snowflake Notebook
How to wrap the model using Snowflake’s CustomModel class
How to log and register the model in the Snowflake ML registry
How to run predictions on new data using the registered model within Snowflake

Related Resources

Updated 2025-12-20

This content is provided as is, and is not maintained on an ongoing basis. It may be out of date with current Snowflake instances