AI Model Performance & Health Dashboard

Drift detection tracks statistical deviation between the data distribution at deployment time and what the model is receiving in production. There are two types: data drift (the inputs have changed) and concept drift (the relationship between inputs and outputs has changed). Both cause silent accuracy degradation — the model answers confidently with increasingly wrong results.

• A model trained on last year's data will degrade when market conditions, language patterns, or customer behaviour shift — drift detection catches this weeks before accuracy visibly collapses
• Data drift is the leading indicator; concept drift is the dangerous one. Without monitoring, you discover failures through customer complaints, not instrumentation
• The Population Stability Index (PSI): below 0.10 is stable, 0.10–0.25 requires investigation, above 0.25 indicates major shift requiring retraining or rollback
• LLMs drift for different reasons than classification models — prompt distribution shifts, topic drift, and language evolution are the primary vectors

1. Capture baseline statistics (mean, variance, feature distributions) on your training or validation set before deployment — this is your reference snapshot
2. Log every inference input to a monitoring store; compute PSI against baseline on a rolling 24-hour window for each feature
3. Set alert at PSI > 0.25; trigger human review at PSI > 0.10 with automated tagging of affected predictions
4. Separate data drift from concept drift by running a labelled validation set weekly — if accuracy drops while PSI is low, you have concept drift

Latency measures how long the model takes to respond — tracked as P50 (median), P95 (95th percentile), and P99 (99th percentile). Uptime measures the percentage of time the system is operational and serving valid responses. For LLMs, two latency metrics matter separately: Time to First Token (TTFT) for perceived responsiveness, and total generation time for workflow completion.

• P95 latency, not average, is what users experience — an average of 200ms means nothing if 5% of requests take 4 seconds and time out upstream workflows
• High latency under load is usually the first signal of capacity issues or model degradation — it precedes accuracy drops and surfacing incidents
• Uptime is a contract commitment for enterprise AI — a 99.9% SLA allows only 8.7 hours downtime per year. Sub-99% in production makes AI systems unreliable as business infrastructure
• Latency spikes at specific input lengths reveal tokenisation bottlenecks — a critical optimisation vector for LLM deployments

1. Instrument every inference call with three timestamps: request received → first token generated → response complete
2. Emit P50, P95, P99 to your observability platform (Datadog, Prometheus, Cloudwatch) in real time — not batched
3. Set SLOs: P95 < 2s for synchronous user-facing workflows; P99 < 5s. Alert at 80% of SLO budget consumed in any hour
4. Monitor latency by input token bucket (0–500, 500–2000, 2000+) — latency scaling by input length is non-linear and exposes capacity constraints
5. Run synthetic health checks every 60 seconds with a fixed test prompt; use these for uptime calculation, not real user traffic

Incident tracking logs every production failure or degradation event, classified by severity. Rollback tracking records when model versions were reverted, why, and how long the decision took. Together, they form your AI governance audit trail — the evidence that your oversight structures actually function rather than existing on paper.

• MTTR (Mean Time to Resolve) is your governance maturity metric — resolving AI incidents in 4 hours versus 4 days reflects the difference between a monitored and an unmonitored deployment
• Rollback history reveals which model versions were stable versus unstable — essential intelligence for future deployment decisions and version management
• Incident logs are a regulatory requirement for AI systems in finance, healthcare, and infrastructure — auditors will ask for them; not having them is a compliance finding, not just a technical gap
• Without automated incident creation, incidents are discovered and reported inconsistently — you cannot measure what you do not systematically detect

1. Define three severity tiers: P0 = complete outage or safety failure; P1 = degraded performance >20% or significant accuracy drop; P2 = anomaly requiring monitoring but not immediate action
2. Automate incident creation when drift or latency thresholds breach — do not rely on manual reporting; human-reported incidents are always late
3. Track MTTD (detection time from incident start) separately from MTTR — detection speed reflects monitoring instrumentation maturity
4. Log every rollback with: version rolled from, version rolled to, trigger reason code, time from decision to deployment completion
5. Run a monthly incident retrospective — classify root causes into data quality, model version, infrastructure, or external dependency; trend these over time