Data Drift vs Concept Drift Detection
Covariate Shift, Population Stability Index (PSI) & Kolmogorov-Smirnov Statistical Testing
#MLOps#Monitoring#Data Drift#Concept Drift#PSI#Covariate Shift
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STAGE 1 / 7— Anti-Pattern
Section 1: The LLM Anti-Pattern vs Right-Sized Model
The Naive Generative LLM Approach:
Deploying an ML model to production and only monitoring infrastructure metrics (CPU, RAM, HTTP 200s) while ignoring feature distribution shifts.
Why It Fails in Production:
Models fail silently! An API can return HTTP 200 in 5ms while outputting completely degraded, disastrous predictions because incoming user data drifted.
Targeted Algorithm (Data Drift vs Concept Drift Detection)
Latency:Automated Daily PSI Scan: 2ms
Cost / 1M Ops:$0.00
Determinism:Statistically Proven Drift Alert
Generative LLM Alternative
Latency:Unmonitored
Cost / 1M Ops:Catastrophic Silent Revenue Loss
Determinism:Silent Failures