The Snowflake MLOps Series — Part 1: The Model Registry

A practical guide to using the native Snowflake Model Registry (snowflake.ml.registry) for MLOps experiment tracking. Contrasts the old approach of hand-rolled SQL tracking tables and stored procedures with Snowflake's modern first-class model versioning primitives. Walks through a complete working example using the TPC-H LINEITEM dataset to predict extended price, training both a Linear Regression and an XGBoost model, logging metrics and artifacts to the registry, and auditing results programmatically. Explains why homegrown tracking schemas break down at scale due to brittle metadata, binary serialization gaps, and lack of input signatures. Part 1 of a series; future installments will cover the Snowflake Feature Store with dbt and GitOps for MLOps.

#machine-learning

#python

#snowflake

#mlops

#xgboost

Jul 14•11m read time•From blog.infostrux.com

Table of contents

Introduction: From Custom Abstractions to Native Execution The MLOps Challenge & The Homegrown Tracking Workaround Why Homegrown Tracking Tables No Longer Cut It The Practical Problem: The TPC-H LINEITEM price prediction Mapping the Abstract Blueprint to Native Implementation Get Mehdi Sidi Boumedine ’s stories in your inbox Step 0: Create the Registry’s Database and Schema Step 1: Setting Up the Baseline Data Environment Step 2: Accessing the Central Repository Step 3: Run #1 — Logging the Baseline Linear Regression Step 4: Run #2 — Iterating with Tuned XGBoost Parameters Auditing the Results Programmatically Setting the Stage for Shifting Feature Sets

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