Building Machine Learning Models on Snowflake

Introduction

Snowflake, a leading cloud-based data platform, has emerged as a powerful environment for building machine learning (ML) models, thanks to its scalable architecture and advanced features like Snowpark ML and native ML functions. As of June 2025, Snowflake enables data scientists and engineers to preprocess data, train models, and deploy predictions within a single platform, eliminating the need for external tools in many cases. This unified approach streamlines ML workflows, enhances scalability, and ensures robust governance. This article explains how to build ML models in Snowflake using Snowpark and SQL-based ML functions, and provides best practices for efficient and secure model development. For more resources, visit snowflake.help.

Why Build ML Models in Snowflake?

Building ML models in Snowflake offers several advantages:

• Unified Platform: Handles data preparation, model training, and inference within one environment, reducing tool sprawl.
• Scalability: Leverages Snowflake’s elastic compute for large-scale data processing and model training.
• Security and Governance: Supports role-based access control (RBAC) and data masking for compliance.
• Flexibility: Combines SQL-based ML with programmatic options via Snowpark (Python, Scala, Java).
• Performance: Utilizes caching and parallel processing for efficient workflows.

However, effective ML development in Snowflake requires optimized data pipelines, secure model management, and proper compute resource allocation to ensure performance and cost-efficiency.

Building ML Models in Snowflake

Snowflake provides a robust set of tools for end-to-end ML workflows, from data preparation to model deployment. Below, we explore key methods, drawing from sources like Snowflake Documentation and Snowflake Summit 2025.

1. Data Preparation with Snowpark

Snowpark enables data preprocessing within Snowflake using Python, Scala, or Java, minimizing data movement and leveraging Snowflake’s compute power.

• Setup:
  • Install the Snowpark library:pip install snowflake-snowpark-python
  • Configure a Snowpark session:from snowflake.snowpark import Session connection_parameters = { "account": "xy12345.us-east-1", "user": "user", "password": "pass", "role": "ml_role", "warehouse": "ml_warehouse", "database": "my_db", "schema": "my_schema" } session = Session.builder.configs(connection_parameters).create()
  • Prepare features for ML:df = session.table("raw_sales").filter("amount > 0").group_by("customer_id").agg({"amount": "sum"}).rename({"SUM(amount)": "total_sales"}) df.write.save_as_table("ml_features")
• Use Case: Aggregate customer purchase data for a churn prediction model.
• Benefits: Executes preprocessing in Snowflake, reducing latency and external dependencies.

2. Snowpark ML for Model Training

Snowpark ML, enhanced in 2025, integrates popular ML libraries like scikit-learn and XGBoost for training models within Snowflake.

• Example: Train a linear regression model for churn prediction:from snowflake.ml.modeling.linear_model import LinearRegression from snowflake.ml.modeling.preprocessing import StandardScaler # Scale features scaler = StandardScaler(input_cols=["total_sales"], output_cols=["scaled_sales"]) scaled_df = scaler.fit(session.table("ml_features")).transform(session.table("ml_features")) # Train model model = LinearRegression(input_cols=["scaled_sales"], label_cols=["churn"], output_cols=["predicted_churn"]) model.fit(scaled_df) # Save model to a stage model.to_sklearn().save_model("@my_stage/churn_model")
• Use Case: Predict customer churn based on historical purchase data.
• Benefits: Leverages Snowflake’s compute scalability and supports integration with familiar ML libraries.

3. SQL-Based ML Functions

Snowflake’s native SQL ML functions enable model training and inference without coding, ideal for analysts comfortable with SQL.

• Example: Train a logistic regression model:CREATE OR REPLACE SNOWFLAKE.ML.CLASSIFICATION model my_classifier USING (SELECT total_sales, churn FROM ml_features) WITH (model_type='logistic_regression');
• Inference:SELECT total_sales, SNOWFLAKE.ML.PREDICT(my_classifier, ARRAY_CONSTRUCT(total_sales)) AS predicted_churn FROM ml_features;
• Use Case: Classify customers as likely to churn using SQL-based workflows.
• Benefits: Simplifies ML for non-programmers and integrates seamlessly with Snowflake’s SQL ecosystem.

4. Model Deployment and Inference

Deploy trained models as user-defined functions (UDFs) for real-time predictions or batch scoring.

• Example: Create a UDF for churn prediction:CREATE OR REPLACE FUNCTION predict_churn(sales FLOAT) RETURNS FLOAT USING PYTHON AS $$ from joblib import load model = load('@my_stage/churn_model') return model.predict([[sales]])[0] $$; SELECT total_sales, predict_churn(total_sales) AS churn_score FROM ml_features;
• Use Case: Score new customer data in real-time for marketing campaigns.
• Benefits: Enables scalable, in-database predictions without external systems.

5. Model Management

Store and manage models in Snowflake stages for versioning and sharing.

• Example: Upload a model to a stage:PUT file://churn_model.joblib @my_stage/churn_model;
• List Models:LIST @my_stage/churn_model;
• Use Case: Maintain versioned models for team collaboration and reproducibility.
• Benefits: Centralizes model storage within Snowflake’s secure environment.

6. Integration with External ML Platforms

For advanced use cases, Snowflake data can be exported to platforms like Databricks or SageMaker:

• Example: Export features to a stage for external use:COPY INTO @my_stage/ml_features.csv FROM ml_features FILE_FORMAT = (TYPE = CSV);
• Use Case: Train complex deep learning models outside Snowflake while leveraging its data preparation.

Best Practices for Building ML Models in Snowflake

To maximize efficiency and security, follow these best practices, informed by sources like ThinkETL and Snowflake Community:

1. Optimize Data Preparation:
   • Use clustering keys to speed up data access for large datasets:ALTER TABLE ml_features ADD CLUSTERING KEY (customer_id);
   • Select only necessary columns to reduce compute usage:SELECT customer_id, total_sales FROM ml_features WHERE churn IS NOT NULL;
2. Secure Models and Data:
   • Restrict access to datasets and models with RBAC:GRANT SELECT ON TABLE ml_features TO ROLE ml_user; GRANT USAGE ON STAGE my_stage TO ROLE ml_user;
   • Use dynamic data masking for sensitive data:CREATE MASKING POLICY sensitive_mask AS (val STRING) RETURNS STRING -> CASE WHEN CURRENT_ROLE() IN ('ML_USER') THEN val ELSE '***MASKED***' END; ALTER TABLE ml_features ALTER COLUMN customer_id SET MASKING POLICY sensitive_mask;
3. Use Dedicated Warehouses:
   • Create ML-specific warehouses to avoid resource contention:CREATE WAREHOUSE ml_warehouse WITH WAREHOUSE_SIZE = 'MEDIUM' AUTO_SUSPEND = 60 AUTO_RESUME = TRUE;
4. Leverage Result Caching:
   • Use Snowflake’s result caching for repetitive preprocessing or inference queries to reduce compute costs:SELECT customer_id, total_sales FROM ml_features WHERE date = '2025-06-18';
5. Automate ML Pipelines:
   • Schedule data preparation and model training with Snowflake Tasks:CREATE TASK feature_engineering_task WAREHOUSE = ml_warehouse SCHEDULE = 'USING CRON 0 0 * * *' AS INSERT INTO ml_features SELECT customer_id, SUM(amount) AS total_sales FROM raw_sales GROUP BY customer_id;
6. Monitor Performance:
   • Track model training and inference performance using Query History:SELECT query_id, query_text, execution_time FROM SNOWFLAKE.ACCOUNT_USAGE.QUERY_HISTORY WHERE warehouse_name = 'ml_warehouse' AND start_time >= DATEADD(hour, -1, CURRENT_TIMESTAMP());
   • Use Query Profile in Snowsight to identify bottlenecks.
7. Version Models:
   • Store models in stages with clear naming conventions to track versions:PUT file://churn_model_v2.joblib @my_stage/churn_model/v2;

Common Challenges and Solutions

Challenge	Solution
Slow data preprocessing	Use Snowpark and clustering keys for efficient data access
High compute costs	Optimize queries, leverage caching, and use auto-suspend
Security risks	Implement RBAC and data masking
Complex model deployment	Deploy models as UDFs for in-database inference
Pipeline automation	Use Snowflake Tasks for scheduled workflows

Conclusion

Snowflake’s Snowpark ML, SQL-based ML functions, and scalable compute architecture make it an ideal platform for building machine learning models, from data preparation to deployment. By leveraging these tools and following best practices—optimizing data preparation, securing models, and automating pipelines—organizations can streamline ML workflows and derive powerful insights. For more resources on Snowflake’s ML capabilities, visit snowflake.help.