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AI Observability: Monitoring & Explainability: Seeing, Understanding, and Trusting Intelligent Systems in Production
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Publisher: Independently Published
Published: Feb 22 2026
Pages: 236
Weight: 1.23
Height: 0.50 Width: 8.50 Depth: 11.00
Language: English
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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