snowflake-ml-python
The machine learning client library that is used for interacting with Snowflake to build machine learning solutions.
Decision gist · record as of 2026-08-14
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.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- 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.
License · maintenance · safety
permissive license (permissive) — 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.
last release 2026-08-12 (2 days) · last repo commit 2026-08-12 · 64 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 993,985 downloads/mo, #4,555 on PyPI
Alternatives
Verify before relying
# 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"])- 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
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 on it.
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
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.
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.
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"])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
| License | permissive license permissive |
| Python support | Supports the current Python release <3.14,>=3.9 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 26 packagesanyiocachetoolscloudpicklefsspech2importlib_resourcesjinja2numpypackagingpandasplatformdirspyarrowpydanticpyjwtpytimeparsepyyamlretryingscikit-learnscipyshapsnowflake-connector-pythonsnowflake-snowpark-pythonsqlparsetqdmtyping-extensionsxgboost |
| Maintenance | Actively maintained 2 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 993,985 / month, #4,555 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 5 - Production/StableEnvironment :: ConsoleEnvironment :: Other EnvironmentIntended Audience :: DevelopersIntended Audience :: EducationIntended Audience :: Information TechnologyIntended Audience :: System AdministratorsLicense :: OSI Approved :: Apache Software LicenseOperating System :: OS IndependentProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.9Topic :: DatabaseTopic :: Scientific/Engineering :: Information AnalysisTopic :: Software DevelopmentTopic :: Software Development :: LibrariesTopic :: Software Development :: Libraries :: Application FrameworksTopic :: Software Development :: Libraries :: Python Modules |
Evidence: snowflake_ml_python-1.51.0-py3-none-any.whl
Tags
Let your AI agent find packages like this
Example. Real query, live index.
You found this page by searching. An agent finds it by wishing: SkillFed indexes 14,416 PyPI packages by what they can do, searchable in plain language.
wish › “machine learning in snowflake”
- snowflake-ml-pythonSnowflake ML Python provides SDKs and infrastructure to build, train,…
- apache-airflow-providers-snowflakeIntegrates Apache Airflow with Snowflake, providing operators, hooks,…
- snowflakeUnified Python API for Snowflake workloads, providing access to data…
Give your agent the search over MCP, or paste the wish link into any chat.
More Software Development packages
Provides backported and experimental type hints for Python 3.9+, allowing use of newer typing features on older Python versions and enabling early experimentation with type system PEPs before they enter the standard library.
NumPy provides an N-dimensional array object and a comprehensive suite of mathematical, linear algebra, Fourier transform, and random number functions for scientific computing in Python.
FastAPI is a Python web framework for building REST APIs using type hints, with automatic request validation, serialization, and interactive API documentation.
Provides a way to document function parameters, class attributes, return types, and variables inline using Python's `Annotated` type hint syntax instead of traditional docstrings.
Typer builds command-line applications from Python functions using type hints, automatically generating help text, argument parsing, and shell completion.
Install it if you are building CLIs in Python.
Distlib provides low-level packaging utilities for building, distributing, and managing Python software—including metadata handling, version specifiers, wheel support, script installation, and dependency resolution.
See also snowflake · snowflake-snowpark-python · snowflake.core · meltanolabs-target-snowflake · mlflow · model-index · azureml-mlflow · kumoai · hopsworks · azureml-core