AI Observability & MLOps
Production AI monitoring, drift detection, evaluation pipelines and continuous model governance.
AI systems degrade silently. Data drift, concept drift, infrastructure degradation and model staleness can cause failures that are invisible without proper observability. This blueprint covers the full production AI observability stack — monitoring, evaluation, alerting, and continuous governance.
When to Use This Blueprint
- Production AI systems with SLAs and business-critical decisions
- Regulated models requiring ongoing performance documentation
- Systems where data distribution shifts are expected over time
- Multi-model environments requiring unified monitoring
- Teams needing to demonstrate ongoing model performance to regulators
Architecture Components
Model Performance Monitoring
Accuracy, precision, recall, AUC tracking over time. Business metric correlation. Cohort-level performance disaggregation.
Data Drift Detection
Population Stability Index, KS test, Jensen-Shannon divergence. Feature distribution shift alerting. Reference dataset management.
Concept Drift Detection
Label drift, prediction distribution shifts, feedback loop detection. Windowed evaluation with statistical significance testing.
LLM Evaluation Pipeline
RAGAS for RAG systems, G-Eval, LLM-as-judge patterns. Automated evaluation datasets, regression testing on model updates.
SLO & Alerting Framework
Latency SLOs (p50/p95/p99), accuracy SLOs, data freshness SLOs. PagerDuty/OpsGenie integration, escalation policies.
Experiment Tracking
MLflow, Weights & Biases, Comet. Hyperparameter logging, artifact management, run comparison, team collaboration.
CI/CD for ML
Automated model testing before deployment. Shadow deployment, canary rollout, A/B testing framework, automated rollback.
Decision Framework
Decision: Monitoring Tooling
Decision: Retraining Strategy
Implementation Phases
Week 1–2
Observability Design
Metric definition, SLO setting, alerting design, tooling selection, baseline dataset capture.
Week 3–5
Monitoring Build
Performance monitoring integration, drift detection setup, LLM evaluation pipeline, dashboard build.
Week 6–7
CI/CD & Automation
Automated testing pipeline, shadow deployment, canary routing, rollback automation.
Week 8
Documentation & Handover
Runbook, SLO documentation, escalation playbook, team training.
Governance Controls
Key Metrics to Track
Need Help Implementing This Blueprint?
Our AI architects can design and implement this architecture for your organisation — governance-first, production-grade and aligned to your specific requirements.