[Verse 1] Your model's sailing smooth in production space But silent storms can shift without a trace Accuracy eroding while you sleep at night Predictions drifting far from what is right Set up your watchtowers with metrics keen Monitor what users have never seen [Chorus] Watch the PSI, catch the drift before it breaks Jensen-Shannon tells you when your data shakes Gradual or sudden, concept shifts will come Alert and retrain when your metrics overcome Keep your sensors sharp, your dashboards bright Monitoring saves models from the fading light [Verse 2] Data drift detection with the KS test Compare distributions, put your fears to rest Population Stability Index as your guide When input features start to slip and slide Confidence calibration shows the truth Your model's certainty needs constant proof [Chorus] Watch the PSI, catch the drift before it breaks Jensen-Shannon tells you when your data shakes Gradual or sudden, concept shifts will come Alert and retrain when your metrics overcome Keep your sensors sharp, your dashboards bright Monitoring saves models from the fading light [Bridge] LLM tokens burning through your budget fast Hallucination creeping, toxicity amassed Infrastructure humming, latency and load Throughput measurements down the system road OpenTelemetry speaks, LangSmith reveals WhyLabs and Arize show what Evidently feels [Verse 3] Concept drift appears in four distinct disguises Gradual decay that slowly compromises Sudden shifts that crash your scores tonight Recurring patterns cycling out of sight Incremental changes building up their case Automated triggers keep you in the race [Final Chorus] Watch the PSI, catch the drift before it breaks Jensen-Shannon tells you when your data shakes Dashboard alerts will sound when thresholds fall Retraining pipelines answer to the call Keep your sensors sharp, your models tight Monitoring guards against the dying light
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