Snowflake-Labs/sfguide-build-end-to-end-ml-workflow-in-snowflake

Snowflake-Labs/sfguide-build-end-to-end-ml-workflow-in-snowflake

Language: Jupyter Notebook

License: Apache-2.0

Stars: 13

Forks: 25

Open issues: 1

Created: 2025-04-09T16:38:44Z

Pushed: 2026-04-22T18:28:41Z

Default branch: main

Fork: no

Archived: no

README:

Quickstart showcasing an end-to-end ML workflow in Snowflake

• Use Feature Store to track engineered features
• Store feature definitions in feature store for reproducible computation of ML features
• Train two SnowML Models
• Baseline XGboost
• XGboost with optimal hyper-parameters identified via Snowflake ML distributed HPO methods
• Track experiments to compare model performance
• Register both models in Snowflake model registry
• Explore model registry capabilities such as metadata tracking, inference, and explainability
• Compare model metrics on train/test set to identify any issues of model performance or overfitting
• Tag the best performing model version as 'default' version
• Set up Model Monitor to track 1 year of predicted and actual loan repayments
• Compute performance metrics such a F1, Precision, Recall
• Inspect model drift (i.e. how much has the average predicted repayment rate changed day-to-day)
• Compare models side-by-side to understand which model should be used in production
• Identify and understand data issues
• Track data and model lineage throughout
• View and understand
• The origin of the data used for computed features
• The data used for model training
• The available model versions being monitored
• Additional components also include
• Distributed GPU model training example
• SPCS deployment for inference
• [WIP] REST API scoring example

INSTRUCTIONS:

Step-by-Step Guide

For prerequisites, environment setup, step-by-step guide and instructions, please refer to the QuickStart Guide.