MLOps is a core function of ML engineering and focuses on streamlining the process of taking machine learning models to production, and then maintaining and monitoring them effectively. Unlike traditional software, machine learning models can change their behavior over time due to various factors, including input drift, outdated assumptions from model training, issues in data pipelines, and standard challenges like hardware/software environments and traffic. These factors can lead to a decline in model performance and unexpected behavior which needs to be monitored very closely.
Snowflake ML provides organizations with an integrated set of capabilities for end-to-end machine learning in a single platform on top of governed data.Model behavior can change over time due to input drift, stale training assumptions, and data pipeline issues, as well as the usual factors, including changes to the underlying hardware and software and the fluid nature of traffic. Snowflake ML Observability feature allows you to track the quality of production models you have deployed via the Snowflake Model Registry across multiple dimensions, such as performance, drift, and volume.
This repository contains a notebook that walks you through building, deploying, and monitoring a customer churn prediction model in Snowflake.
For prerequisites, environment setup, step-by-step guide and instructions, please refer to the QuickStart Guide.