Continuous Model Evaluation
Ongoing pipeline to detect model performance degradation over time
Intent & Description
🎯 Intent
Detect when a model’s real-world performance degrades after deployment due to input distribution shifts or relationship changes between inputs and outcomes.
📋 Context
A model’s real-world performance can quietly degrade after deployment—the input distribution can shift, or the relationship between inputs and true outcome can change. Without ongoing evaluation, this decay goes unnoticed until it causes damage.
💡 Solution
Set up an ongoing pipeline that logs live predictions, reconciles them against actual outcomes once available, and computes evaluation metrics on a rolling basis. This catches degradation automatically, distinguishes genuine decay from normal noise, and provides evidence triggering retraining decisions.
Real-world Use Case
- Production ML systems in dynamic environments
- Models subject to data drift or concept drift
- High-stakes predictions where degradation is costly
- Any production system requiring ongoing monitoring
Source
📌 TL;DR
Implement ongoing evaluation pipelines that log predictions, reconcile with ground truth, and compute metrics on rolling windows to detect performance degradation automatically
Advantages
- Automatic detection of performance degradation
- Distinguishes real decay from normal noise
- Provides evidence for retraining decisions
- Can slice evaluation by relevant segments
Disadvantages
- Depends on reliable ground-truth collection
- Ground truth can be slow, incomplete, or expensive
- Requires infrastructure for logging and reconciliation
- Needs alerting and escalation processes