DriftDeck

Overview

A monitoring dashboard for retrained machine-learning models, tracking performance, feature drift, and threshold stability over time.

Goals

• Keep retrained models observable in production over time.
• Detect feature drift and threshold instability before they degrade decisions.
• Give a single place to judge model health.

Approach

• Instrument models to emit performance and drift metrics.
• Apply statistical tests for feature drift and threshold stability.
• Visualize trends and alerts in a monitoring dashboard.

Status

🚧 In progress. A full write-up — methodology, results, and code — is coming soon.