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Monitoring & Observability

An AI Product must be observable in operation.
Monitoring and observability ensure that its performance, safety, and compliance are continuously evaluated.
Without observability, an AI Product cannot be trusted, governed, or evolved responsibly.

Why Monitoring & Observability Matter​

  • Trustworthiness → Detect bias, drift, errors, and anomalies.
  • Compliance → Prove alignment with regulations and ethical standards.
  • Reliability → Ensure uptime, latency, and throughput meet declared SLOs.
  • Evolution → Enable retraining and continuous improvement.

Required Monitoring Dimensions​

AI Products must expose signals across several dimensions:

  1. Operational Health

    • Availability, uptime, error rates.
    • Latency, throughput, resource utilization (CPU, GPU, memory).

  2. Model Performance

    • Accuracy, precision/recall, F1 score, or other relevant benchmarks.
    • Domain-specific KPIs (e.g., BLEU for translation, ROUGE for summarization).

  3. Bias & Fairness

    • Group fairness metrics (demographic parity, equalized odds).
    • Drift in fairness over time.

  4. Drift Detection

    • Data Drift → changes in input data distributions.
    • Concept Drift → changes in relationships between inputs and outputs.
    • Model Drift → performance degradation against ground truth or benchmarks.

  5. Explainability Signals

    • Feature importance, attention maps, rationale traces.
    • Links to model or system cards.

  6. Security & Abuse

    • Adversarial input detection.
    • Abuse monitoring (prompt injection, malicious queries).

Observability Mechanisms​

AI Products must provide monitoring through:

  • APIs / Metrics Endpoints (e.g., Prometheus, OpenTelemetry).
  • Dashboards for human monitoring.
  • Alerts for threshold violations (latency, drift, fairness breaches).
  • Logs & Traces for root-cause analysis.

All observability signals must be:

  • Machine-readable → enabling automation.
  • Auditable → recorded for compliance evidence.
  • Accessible → available to both product owners and governance bodies.

Example​

Vision Classification Product

  • Operational Health: 99.9% uptime, latency 50ms avg.
  • Model Performance: 92% accuracy on validation set.
  • Bias & Fairness: Monitored across age and gender groups.
  • Drift Detection: Alerts if input image distributions deviate by >10%.
  • Explainability: Grad-CAM heatmaps exposed as API option.
  • Security: Logs adversarial perturbation attempts.

Summary​

  • Monitoring and observability are mandatory for AI Products.
  • They cover health, performance, fairness, drift, explainability, and security.
  • Signals must be machine-readable, auditable, and accessible.

Principle: An AI Product without observability is not a trustworthy product — it is only an asset in disguise.