Beyond Storage: Transforming Snowflake into an End-to-End ML Platform
For years, the standard machine learning architecture has been a complex dance of data movement. Data engineers extract information from Snowflake to feature stores, data scientists download samples to their laptops for experimentation, MLOps teams reingest everything into training platforms, and yet another system handles deployment. Each data hop introduces latency, security risks, and governance challenges.
But what if you didn't have to move your data at all?
Snowflake has quietly evolved from a data warehouse into a legitimate ML platform, allowing organizations to consolidate their ML infrastructure and keep workloads where their data already lives. This transformation is saving engineering teams thousands of development hours while dramatically improving model performance and governance.
In this article, I'll walk through how leading engineering teams are building complete ML pipelines within Snowflake, share concrete implementation patterns, and explain why this architectural shift represents a fundamental rethinking of the ML tech stack.
The New Snowflake ML Stack
The evolution of Snowflake's ML capabilities didn't happen overnight. It's been the result of strategic platform additions:
Together, these tools create an end-to-end platform that covers every step in the ML lifecycle. Let's explore a complete architecture that teams are implementing today.
A Real-World ML Architecture Within Snowflake
1. Feature Engineering with Snowpark for Python
Feature engineering has traditionally been Snowflake's sweet spot, but Snowpark for Python takes it to a new level. Rather than just using SQL, data scientists can now use familiar Python libraries directly on Snowflake data.
Here's a real-world example that would traditionally require extracting data to an external Python environment:
import snowflake.snowpark as snowpark
from snowflake.snowpark.functions import col, datediff, avg, stddev
def engineer_customer_features(session):
# Load customer transaction data
transactions = session.table("transactions")
customers = session.table("customers")
# Calculate recency, frequency, monetary features
current_date = session.sql("SELECT CURRENT_DATE() as today").collect()[0]["TODAY"]
rfm_features = transactions.group_by("customer_id").agg(
datediff("day", max(col("transaction_date")), current_date).alias("recency"),
count("transaction_id").alias("frequency"),
avg("amount").alias("avg_purchase"),
stddev("amount").alias("std_purchase")
)
# Apply advanced transformations using pandas
def apply_log_transform(pdf):
# Log transformation to handle skewed monetary values
pdf["log_avg_purchase"] = np.log1p(pdf["avg_purchase"])
return pdf
rfm_features_transformed = rfm_features.to_pandas_on_spark(
index_col="customer_id"
).grouped_map(apply_log_transform)
# Join with customer demographics
customer_features = rfm_features_transformed.join(
customers, rfm_features_transformed["customer_id"] == customers["id"]
)
# Create finalized feature table
customer_features.write.save_as_table("customer_features")
return customer_features
This code runs entirely within Snowflake using a Snowpark session, with no data movement required. The to_pandas_on_spark() function allows you to use pandas-style operations on Snowflake data, giving data scientists familiar tools while keeping data in place.
2. Model Training with Scikit-learn in Snowflake
With features prepared, we can train models directly in Snowflake:
import snowflake.snowpark as snowpark
from snowflake.snowpark.types import FloatType, VariantType
from snowflake.ml.modeling.preprocessing import StandardScaler
from sklearn.ensemble import RandomForestClassifier
import pickle
def train_churn_model(session):
# Load feature table
features = session.table("customer_features")
# Split into training and validation sets
train, validation = features.random_split([0.8, 0.2], seed=42)
# Prepare features and target
feature_cols = ["recency", "frequency", "log_avg_purchase", "std_purchase",
"tenure", "age", "total_products"]
target_col = "has_churned"
X_train = train.select(feature_cols).to_pandas()
y_train = train.select(target_col).to_pandas()
X_val = validation.select(feature_cols).to_pandas()
y_val = validation.select(target_col).to_pandas()
# Preprocessing with Snowpark ML
scaler = StandardScaler()
X_train_scaled = scaler.fit_transform(X_train)
X_val_scaled = scaler.transform(X_val)
# Train model
model = RandomForestClassifier(n_estimators=100, max_depth=10)
model.fit(X_train_scaled, y_train)
# Evaluate model
val_accuracy = model.score(X_val_scaled, y_val)
print(f"Validation accuracy: {val_accuracy:.4f}")
# Serialize the trained model
model_bytes = pickle.dumps(model)
preprocessing_bytes = pickle.dumps(scaler)
# Store model in Snowflake
session.sql(f"""
CREATE OR REPLACE TABLE models.churn_prediction (
model_id VARCHAR,
model_version VARCHAR,
training_date TIMESTAMP_NTZ,
model BINARY,
preprocessor BINARY,
accuracy FLOAT,
feature_columns VARIANT,
description VARCHAR
)
""").collect()
session.sql(f"""
INSERT INTO models.churn_prediction VALUES (
'customer_churn',
'1.0',
CURRENT_TIMESTAMP(),
{session.lit(model_bytes)},
{session.lit(preprocessing_bytes)},
{val_accuracy},
PARSE_JSON('{feature_cols}'),
'Random Forest churn prediction model'
)
""").collect()
return {
"model_id": "customer_churn",
"version": "1.0",
"accuracy": val_accuracy
}
This code demonstrates:
No data leaves the Snowflake environment at any point, maintaining governance and eliminating transfer overhead.
3. Model Deployment with Snowflake UDFs
Once trained, we can deploy the model as a Snowflake User-Defined Function:
import snowflake.snowpark as snowpark
import pickle
import numpy as np
def create_prediction_udf(session):
# Create UDF for real-time prediction
session.sql("""
CREATE OR REPLACE FUNCTION predict_churn(
recency FLOAT,
frequency INT,
avg_purchase FLOAT,
std_purchase FLOAT,
tenure INT,
age INT,
total_products INT
)
RETURNS FLOAT
LANGUAGE PYTHON
RUNTIME_VERSION=3.8
HANDLER='predict'
IMPORTS=('@ml_models/churn_model.pkl',
'@ml_models/preprocessor.pkl')
AS
$$
import pickle
import sys
import numpy as np
def predict(recency, frequency, avg_purchase, std_purchase, tenure, age, total_products):
# Load model and preprocessor
import_dir = sys._xoptions.get('import_directory')
with open(import_dir + 'churn_model.pkl', 'rb') as f:
model = pickle.load(f)
with open(import_dir + 'preprocessor.pkl', 'rb') as f:
preprocessor = pickle.load(f)
# Apply log transform to monetary value
log_avg_purchase = np.log1p(avg_purchase)
# Prepare input features
features = np.array([[recency, frequency, log_avg_purchase, std_purchase,
tenure, age, total_products]])
# Apply preprocessing
features_scaled = preprocessor.transform(features)
# Generate prediction
churn_probability = model.predict_proba(features_scaled)[0, 1]
return float(churn_probability)
$$
""").collect()
# Export model to stage for UDF to access
model_row = session.sql("""
SELECT model, preprocessor
FROM models.churn_prediction
WHERE model_id = 'customer_churn'
ORDER BY training_date DESC
LIMIT 1
""").collect()[0]
# Write model files to a stage
session.file.put(model_row["MODEL"], "@ml_models/churn_model.pkl", overwrite=True)
session.file.put(model_row["PREPROCESSOR"], "@ml_models/preprocessor.pkl", overwrite=True)
# Test the deployed model
test_prediction = session.sql("""
SELECT predict_churn(30, 5, 120.50, 35.2, 24, 38, 2) AS churn_probability
""").collect()[0]["CHURN_PROBABILITY"]
print(f"Test prediction result: {test_prediction:.4f}")
return {"status": "Model deployed successfully as UDF"}
This function:
Now anyone can call predict_churn() in a SQL query to get real-time predictions:
-- Get churn probabilities for all customers
SELECT
c.customer_id,
c.email,
c.name,
predict_churn(
f.recency,
f.frequency,
f.avg_purchase,
f.std_purchase,
f.tenure,
f.age,
f.total_products
) AS churn_probability
FROM customers c
JOIN customer_features f ON c.customer_id = f.customer_id
WHERE c.status = 'active'
ORDER BY churn_probability DESC
LIMIT 100;
4. Building ML Applications with Streamlit in Snowflake
With models deployed, the final piece is creating applications that leverage predictions. Streamlit in Snowflake lets you build ML applications directly connected to your data and models:
# Save as app.py in a Snowflake stage
import streamlit as st
import snowflake.snowpark as snowpark
import pandas as pd
import plotly.express as px
# Initialize session
session = snowpark.Session.builder.getOrCreate()
st.title("Customer Churn Prediction Dashboard")
# Sidebar filters
st.sidebar.header("Filters")
min_probability = st.sidebar.slider("Minimum Churn Probability", 0.0, 1.0, 0.7)
segments = st.sidebar.multiselect("Customer Segments",
["Premium", "Standard", "Basic"],
default=["Premium", "Standard", "Basic"])
# Query high-risk customers
high_risk_customers = session.sql(f"""
SELECT
c.customer_id,
c.name,
c.email,
c.segment,
c.signup_date,
predict_churn(
f.recency,
f.frequency,
f.avg_purchase,
f.std_purchase,
f.tenure,
f.age,
f.total_products
) AS churn_probability
FROM customers c
JOIN customer_features f ON c.customer_id = f.customer_id
WHERE c.status = 'active'
AND c.segment IN ({','.join([f"'{s}'" for s in segments])})
HAVING churn_probability >= {min_probability}
ORDER BY churn_probability DESC
LIMIT 500
""").to_pandas()
# Display metrics
st.header("Churn Risk Overview")
col1, col2, col3 = st.columns(3)
col1.metric("High Risk Customers", len(high_risk_customers))
col2.metric("Average Churn Probability", f"{high_risk_customers['CHURN_PROBABILITY'].mean():.2f}")
col3.metric("Premium Customers at Risk",
len(high_risk_customers[high_risk_customers['SEGMENT'] == 'Premium']))
# Show distribution chart
st.subheader("Churn Probability Distribution")
fig = px.histogram(high_risk_customers, x="CHURN_PROBABILITY",
color="SEGMENT", nbins=20,
title="Distribution of Churn Probabilities by Segment")
st.plotly_chart(fig)
# Customer list with action items
st.subheader("Customers at Highest Risk")
st.dataframe(high_risk_customers[['NAME', 'EMAIL', 'SEGMENT', 'CHURN_PROBABILITY']])
# Enable actions (would connect to other systems in production)
if st.button("Generate Retention Offers for Selected Customers"):
st.success("Retention offers generated and sent to marketing team!")
This Streamlit application:
All this runs natively within Snowflake, with no separate application server required.
Integrating External ML Services for Advanced AI
While Snowflake can handle many ML workloads natively, sometimes you need specialized capabilities from cloud ML services. External functions provide a bridge without sacrificing governance:
-- Create an API integration for AWS SageMaker
CREATE OR REPLACE API INTEGRATION sagemaker_integration
API_PROVIDER = AWS_API_GATEWAY
API_AWS_ROLE_ARN = 'arn:aws:iam::123456789012:role/snowflake-external-func-role'
ENABLED = TRUE
API_ALLOWED_PREFIXES = ('https://xxxxx.execute-api.us-west-2.amazonaws.com/prod/');
-- Create external function that calls a SageMaker endpoint
CREATE OR REPLACE EXTERNAL FUNCTION image_classification(image_data VARCHAR)
RETURNS VARIANT
API_INTEGRATION = sagemaker_integration
AS 'https://xxxxx.execute-api.us-west-2.amazonaws.com/prod/classify';
-- Use the external function
SELECT
product_id,
product_name,
image_url,
image_classification(image_data) AS classification
FROM product_catalog
LIMIT 10;
This pattern allows you to:
Vector Search for AI and LLM Applications
For teams building AI applications, Snowflake now offers vector search capabilities:
-- Create a table with vector embeddings
CREATE OR REPLACE TABLE document_embeddings (
document_id VARCHAR,
title VARCHAR,
content VARCHAR,
embedding VECTOR(1536)
);
-- Insert document embeddings (in production, these would be generated via an external function)
INSERT INTO document_embeddings
VALUES
('doc1', 'Snowflake ML Capabilities', 'Snowflake provides ML capabilities...',
CAST(ARRAY_CONSTRUCT(0.021, -0.14, ...) AS VECTOR(1536))),
('doc2', 'Data Engineering Best Practices', 'When building data pipelines...',
CAST(ARRAY_CONSTRUCT(0.053, 0.02, ...) AS VECTOR(1536)));
-- Perform vector similarity search
SELECT
document_id,
title,
content,
VECTOR_COSINE_SIMILARITY(embedding,
CAST(ARRAY_CONSTRUCT(0.035, -0.12, ...) AS VECTOR(1536))) AS similarity
FROM document_embeddings
ORDER BY similarity DESC
LIMIT 5;
This enables semantic search, recommendation engines, and other AI-powered features directly within Snowflake.
Performance and Governance Benefits
The business case for consolidating ML workloads in Snowflake goes beyond technical elegance. Organizations are seeing concrete benefits:
1. Reduced Data Movement Overhead
A financial services company I worked with calculated that their previous architecture spent 40% of total compute resources just moving data between systems. By consolidating in Snowflake, they eliminated these transfer costs entirely.
2. Better Governance and Compliance
With ML assets (data, features, models) in one platform, governance becomes dramatically simpler:
This is especially valuable in regulated industries where demonstrating model governance is a legal requirement.
3. Improved Time-to-Production
A retail client reduced their ML deployment cycle from 45 days to 7 days by eliminating integration work between systems. Their architecture now looks like this:
![Snowflake ML Architecture Diagram showing consolidated workflow]
4. Lower Infrastructure Costs
By eliminating the need for separate feature stores, model training clusters, and serving infrastructure, teams are seeing 30-50% reductions in total infrastructure costs. One e-commerce company saved $350,000 annually by consolidating ML workloads into their existing Snowflake environment.
The Future: Snowflake as a Complete ML Platform
As Snowflake continues to expand its ML capabilities, we're approaching a future where the default architecture for many ML use cases will be Snowflake-centric. The roadmap items to watch:
Getting Started with ML in Snowflake
If you're interested in consolidating your ML workloads in Snowflake, here's how to begin:
The most successful teams begin with a single high-value use case, prove the approach, and then expand to more complex ML scenarios.
Conclusion: The Coming ML Architecture Consolidation
The multi-platform ML architecture that's been standard for years is giving way to a more consolidated approach. By keeping ML workloads close to data, organizations are seeing dramatic improvements in development velocity, governance, and infrastructure costs.
Snowflake's transformation from data warehouse to ML platform represents a fundamental shift in how teams can build and deploy ML capabilities. Those who embrace this architectural evolution will have a significant advantage in their ability to deliver ML-powered features efficiently and at scale.
What ML workloads are you considering moving to Snowflake? Have you had success with any of these approaches? Share your experiences in the comments.
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