Quickstarts¶

Use the following quickstarts to help you get up to speed with Snowflake ML.

End to end examples¶
Quickstart	Level	Description
Build an End-to-End ML Model in Snowflake	Beginner	Build, deploy and manage an XGBoost model in production, including full intro of Snowflake’s MLOps capabilities
Scale Embeddings with Snowflake Notebooks on Container Runtime	Intermediate	Experiment with an open source embedding model and serve for large batch inference
Defect Detection Using Distributed PyTorch with Snowflake Notebooks	Intermediate	Detect defects with PyTorch-based computer vision models using GPUs
Getting Started with Distributed PyTorch with Snowflake Notebooks	Intermediate	Build and deploy a recommendation model with PyTorch using GPUs
Building ML Models to Crack the Code of Customer Conversions	Intermediate	Build a complete ML pipeline that classifies text data, performs sentiment analysis with gen AI, and predicts customer purchases using XGBoost

Model development examples¶
Quickstart	Level	Description
Getting Started with Snowflake Notebooks on Container Runtime	Beginner	Introductory quickstart covering the basics of using Snowflake Notebooks on Container Runtime
Getting Started with ML Development in Snowflake	Beginner	Develop a model in Snowflake Notebooks, including preprocessing, feature engineering and model training
Train an XGBoost Model with GPUs using Snowflake Notebooks	Beginner	Train an XGBoost model on GPUs in Snowflake Notebooks
Distributed Multi-Node and Multi GPU Audio Transcription with Snowflake ML	Intermediate	Perform multi-node, multi-GPU audio transcriptions using Container Runtime with OpenAI’s Whisper’s large-v3 on HuggingFace

MLOps examples¶
Quickstart	Level	Description
Introduction to Snowflake Feature Store with Snowflake Notebooks	Beginner	Introductory quickstart covering the basics of using Snowflake Feature Store
Getting Started with Snowflake Feature Store API	Beginner	Introductory quickstart covering the basics of using APIs in Snowflake Feature Store
Getting Started with ML Observability in Snowflake	Beginner	Introductory quickstart covering the basics of using ML Observability in Snowflake
Develop and Manage ML Models with Feature Store and Model Registry	Intermediate	Demonstrates an ML experiment cycle including feature creation, training data generation, model training and inference