Overview

AI systems don't fail loudly. They fail silently. Models drift. Bias creeps in. Predictions look confident-until trust collapses. Traditional monitoring can't see it. Accuracy can't explain it. And dashboards can't defend it. AI Observability: Monitoring & Explainability is the definitive guide to building AI systems you can see, understand, govern, and trust-in production, at scale, and under scrutiny. This book goes far beyond theory. You'll learn how to: Detect data drift, bias, and silent model decay Monitor predictions, confidence, and real user impact Explain AI decisions clearly to users, auditors, and regulators Design end-to-end AI observability architectures Handle AI incidents, audits, and governance with evidence-not excuses Apply observability to GenAI and foundation models Written for real-world engineers, architects, leaders, and auditors, this book transforms AI from a black box into an accountable system. If you deploy AI in production, this book is no longer optional.

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