Machine Learning with Snowflake ML

Machine Learning with Snowflake ML

Snowflake ML is a machine learning framework built directly into the Snowflake platform. It allows users to build, train, and deploy ML models using both SQL and Python, all without needing to move data outside the Snowflake environment. This tight integration reduces data transfer overhead, improves security, and accelerates the overall ML workflow. With Snowflake ML, teams can handle everything—from data preparation and feature engineering to model training and inference—within a single, unified platform.

Let’s explore how we can integrate ML into our current workflow!

What is Snowflake ML?

• Snowflake ML is an integrated suite of features and tools for building and deploying machine learning models directly within Snowflake’s data platform.
• It combines:
  • In-database ML functions (like ML.PREDICT)
  • Python-based ML workflows using Snowpark for Python
  • Integration with external frameworks (like scikit-learn, XGBoost, etc.)
• It allows data engineers and data scientists to work with ML models where the data lives, avoiding data movement and reducing complexity.

Benefits of Snowflake ML

• Simplifies ML workflows by using familiar SQL and Python.
• Eliminates data movement, which can introduce latency and security risks.
• Use Snowflake’s scalability to handle large datasets.
• Supports MLOps practices, making it easier to deploy and monitor models in production.

How Snowflake ML Works: Step by Step

• Data Preparation with Snowflake
  • Use Snowflake’s SQL and Snowpark DataFrame APIs to:
    • Clean data (handle missing values, outliers)
    • Join data from multiple tables
    • Create training and test datasets

Example:

• Feature Engineering with Snowpark for Python
  • Use Snowpark’s Python API for advanced transformations:
    

• Model Training: In-Snowflake or External
  • Option A: In-Snowflake ML Functions
    • Use Snowflake ML functions to train models in SQL.
      

• • Option B: Snowpark for Python + External Libraries
    • Use Snowpark’s Python API to load data into pandas and train models with scikit-learn.
      

• Model Deployment and Prediction
  • Using In-Snowflake ML for predictions:

• • Using Python UDFs for external models:
    

• Monitoring and Retraining
  • Use Snowflake’s Task feature to schedule model retraining.
  • Monitor performance using:
    • Model evaluation metrics
    • Regular validation on fresh data

Real-Life Example: Predicting Customer Churn

Let’s say we’re a SaaS company using Snowflake to manage customer data. We want to predict which customers are likely to churn based on usage patterns.

• Step 1: Clean and join usage logs and customer profiles in Snowflake.
• Step 2: Use Snowpark to create features like:
  • Average monthly login counts
  • Number of support tickets
• Step 3: Train a classification model (e.g., logistic regression) using Snowpark for Python.
• Step 4: Deploy the model with a UDF, running predictions in Snowflake.
• Step 5: Use the predictions to trigger proactive retention campaigns.
  

OUTPUT : Accuracy: 0.85

Key Benefits 

• No data movement: Models run where the data lives.
• Unified environment: Use SQL and Python together.
• Faster deployment: Skip the hassle of moving data to external ML platforms.
• Scalability: Use Snowflake’s compute power to train models on large datasets.

Best Practices with Snowflake ML

• Start with small prototypes using sample data to validate your approach.
• Use Snowflake’s built-in ML functions for simpler use cases.
• Switch to external libraries for more advanced models, using Python UDFs.
• Monitor model performance regularly and retrain as needed.
• Collaborate across teams—data engineers, analysts, and scientists—to get the most out of our data.