snowflake-ml-python

What it is and what it does

Snowflake ML Python is an official SDK for building end-to-end machine learning workflows within Snowflake's data warehouse. It spans model development (preprocessing, feature engineering, training with sklearn/xgboost/lightgbm), MLOps infrastructure (model registry, feature store, versioned datasets), and experiment tracking. The package runs computations directly on Snowflake's infrastructure, letting you train models on large datasets without moving data out of the warehouse.

The SDK is tightly integrated with Snowflake's ecosystem: it uses snowflake-connector-python and snowflake-snowpark-python for data access, and includes framework connectors for PyTorch and TensorFlow. It is pre-installed in Snowflake Container Runtime notebooks and can be installed via conda (from Snowflake's or conda-forge channels) or pip for use in external Python environments. The package is production-stable and actively developed.

Use it for:

• Train sklearn or xgboost models on large Snowflake tables without exporting data to a local machine
• Build and manage a feature store within Snowflake with automated incremental refresh from batch or streaming sources
• Log, version, and deploy trained models using the Snowflake Model Registry with MLflow 3.x support
• Perform experiment tracking and run management with automatic source provenance capture for reproducibility
• Preprocess and transform large datasets using Snowpark Optimized High Memory Warehouses for scalable feature engineering

Worth the install?

AI-flagged interpretation of the facts on this page — verify before relying

Snowflake ML Python provides SDKs and infrastructure to build, train, manage, and deploy machine learning models directly within Snowflake, covering data preprocessing, feature engineering, model development, experiment tracking, and model registry.

Yes, if you use Snowflake and want to build ML workflows without exporting data. The package is production-stable, actively maintained, and Apache 2.0 licensed. Install friction is low and there are no known vulnerabilities. Requires a Snowflake account and familiarity with the Snowflake ecosystem; not suitable for standalone ML work outside Snowflake.

Install

snowflake-ml-python on PyPI

pip install snowflake-ml-python

uv add snowflake-ml-python

poetry add snowflake-ml-python

Installing snowflake-ml-python

Before you install

Low install friction with a pure-Python wheel distribution. Actively maintained with a release 2 days old. Supports Python 3.9 through 3.12. Brings 26 runtime dependencies including numpy, pandas, scikit-learn, xgboost, and snowflake-connector-python, which is expected for an ML platform integration.

License in practice

Licensed under Apache 2.0 (permissive). You may use, modify, and distribute the package freely in commercial and private projects, provided you include a copy of the license and document any modifications.

Quickstart

# Install
pip install snowflake-ml-python

# Basic usage
from snowflake.ml.modeling.preprocessing import StandardScaler
from snowflake.ml.modeling.ensemble import RandomForestClassifier
from snowflake.snowpark.session import Session

session = Session.builder.config("connection_name", "my_connection").create()
scaler = StandardScaler(input_cols=["feature1"], output_cols=["feature1_scaled"])
model = RandomForestClassifier(input_cols=["feature1_scaled"], label_cols=["target"])

Requires an active Snowflake account and valid connection credentials. Python 3.9–3.12 only. snowflake-connector-python and snowflake-snowpark-python must be available in your environment.

Verify before relying

• Whether the package works equally well outside Snowflake Container Runtime notebooks or if performance/features differ significantly in standard environments
• Whether all 26 dependencies are always required or if some are optional for specific use cases

Package facts

Evidence: snowflake_ml_python-1.51.0-py3-none-any.whl

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