Managing ML Models from Iteration to Production with MLOps
Overview
This solution architecture shows you how to manage Machine learning models from experimentation to production using Snowflake MLOps features
- Snowpark ML Modeling for feature engineering and model training with familiar Python frameworks
- Snowflake Feature Store for continuous, automated refreshes on batch or streaming data
- Snowflake Model Registry to version control models and their metadata
- ML Lineage to trace end-to-end feature and model lineage (currently in private preview)
Solution Architecture: MLOps in Snowflake
- Load diamonds quality dataset into a Snowflake table and create features view to include relevant feature for model training
- Use Snowpark dataframe API for feature creation and data transformation, and Snowpark ML API to train an XGB Regressor
- Experiment with the model training and log different model versions in model registry
- Run inference on a select model from the registry
- Track model lineage to capture the training set, features, model parameters, etc for every model in the registry
Get Started
Updated 2026-04-28
This content is provided as is, and is not maintained on an ongoing basis. It may be out of date with current Snowflake instances
