{"categories":[{"label":"Software Development","url":"https://skillfed.io/packages/category/software-development/6"},{"label":"Libraries","url":"https://skillfed.io/packages/category/software-development-libraries/4"},{"label":"Python Modules","url":"https://skillfed.io/packages/category/software-development-libraries-python-modules/8"},{"label":"Database","url":"https://skillfed.io/packages/category/database/2"},{"label":"Application Frameworks","url":"https://skillfed.io/packages/category/software-development-libraries-application-frameworks/2"},{"label":"Information Analysis","url":"https://skillfed.io/packages/category/scientific-engineering-information-analysis"}],"enrichment":{"capability":"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.","skillfed_tags":["snowflake-integration","mlops","feature-store"],"use_cases":["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"],"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.\n\nThe 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.","worth_installing":"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."},"id":"snowflake-ml-python","links":{"html":"https://skillfed.io/packages/snowflake-ml-python","md":"https://skillfed.io/packages/snowflake-ml-python.md","pypi":"https://pypi.org/project/snowflake-ml-python/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-08-12","license_spdx":null,"license_treatment":"permissive","name":"snowflake-ml-python","python_support":"supports_current","summary":"The machine learning client library that is used for interacting with Snowflake to build machine learning solutions."},"popularity":{"monthly_downloads":993985,"position":4555,"tier":"top_5000"},"security":{"n_vulnerabilities":0},"version":"1.51.0"}