Snowflake-Labs/sfguide-from-dev-to-production-why-ml-teams-are-migrating-to-snowflake

Snowflake-Labs/sfguide-from-dev-to-production-why-ml-teams-are-migrating-to-snowflake

Language: Jupyter Notebook

License: Apache-2.0

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Created: 2025-10-22T00:24:44Z

Pushed: 2026-02-02T23:53:11Z

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README:

From Dev to Production: Why ML team are migrating to Snowflake

Overview

In this guide, you'll learn how to build a complete machine learning lifecycle in Snowflake, from model development to production deployment. You'll deploy HuggingFace models, train custom ML models, track experiments, deploy for inference, and enable real-time feature serving. The application addresses end-to-end ML development showing how to do audio processing, feature extraction, model training, deployment, and monitoring all inside Snowflake with unified governance across the application full-stack.

Step-By-Step Guide

For prerequisites, environment setup and instructions, refer to the QuickStart Guide.