Drift Monitor
Sistem Analizi
Normal Davranış
The drift monitor ingests streaming inferences from production serving nodes, computes statistical summary profiles over rolling windows (e.g. hourly or daily), and compares the distribution of each feature against the training baseline. If divergence exceeds a configured threshold, it dispatches warnings to on-call engineers.
Çöküş Davranışı
If the reference baseline is outdated, sample windows are too small, or features with high natural volatility lack adjusted thresholds, the drift monitor either generates overwhelming false-positive alert fatigue or fails to detect silent concept drift until business revenue drops.
İş Sonuçları
Unknown
Bilinen İsimler
Teknik Terminoloji
Hata Göstergeleri
Sistem Mimarisi
FAQ
Normalde nasıl davranır?
The drift monitor ingests streaming inferences from production serving nodes, computes statistical summary profiles over rolling windows (e.g. hourly or daily), and compares the distribution of each feature against the training baseline. If divergence exceeds a configured threshold, it dispatches warnings to on-call engineers.
Nasıl çöker?
If the reference baseline is outdated, sample windows are too small, or features with high natural volatility lack adjusted thresholds, the drift monitor either generates overwhelming false-positive alert fatigue or fails to detect silent concept drift until business revenue drops.
İş sonuçları nelerdir?
Unknown
What is the difference between data drift and concept drift?
Data drift (or feature drift) occurs when the statistical distribution of the input features $P(X)$ changes over time, while concept drift occurs when the underlying mathematical relationship between features and the target label $P(Y|X)$ changes, causing the model's learned logic to become invalid even if input features look familiar.
How does Population Stability Index (PSI) quantify drift?
PSI divides the feature distribution into bins and calculates $\sum (Actual\% - Expected\%) \times \ln(Actual\% / Expected\%)$. A PSI value below 0.1 indicates minimal shift, 0.1 to 0.2 indicates moderate change requiring monitoring, and above 0.2 represents significant distribution drift requiring model retraining.
Sistemi keşfet
AI özeti
Drift Monitor is a undefined system in TinyCTO.tv. The drift monitor ingests streaming inferences from production serving nodes, computes statistical summary profiles over rolling windows (e.g. hourly or daily), and compares the distribution of each feature against the training baseline. If divergence exceeds a configured threshold, it dispatches warnings to on-call engineers.