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
• 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.