Automated Testing for AI Systems
Building reliable AI with continuous testing, behavioral validation, and real-time guardrails (English Edition)
$53.28
- Paperback
296 pages
- Release Date
28 October 2026
Summary
As AI transitions from experimental projects into mission-critical applications across modern industries, traditional software testing techniques are proving inadequate for probabilistic machine learning, generative AI, and agentic AI systems. This book addresses this reliability gap by introducing AI TestOps-a specialized discipline designed to eliminate AI Quality Debt and build scalable AI solutions.
This book introduces AI TestOps, a practical engineering discipline for testing AI…
Book Details
| ISBN-13: | 9788167081285 |
|---|---|
| ISBN-10: | 8167081281 |
| Author: | Lekshmi Pillai Chidambarathanu |
| Publisher: | Bpb Publications |
| Imprint: | Bpb Publications |
| Format: | Paperback |
| Number of Pages: | 296 |
| Release Date: | 28 October 2026 |
| Weight: | 513g |
| Dimensions: | 16mm x 191mm x 235mm |
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Lekshmi Pillai Chidambarathanu
Lekshmi Pillai Chidambarathanu is a software engineering and transformation leader with more than 20 years of experience driving enterprise technology strategy and leading global engineering organizations across Fortune 100 companies. Her expertise spans cloud platforms, artificial intelligence, MLOps, and modern software engineering.
Throughout her career, she has led large-scale digital transformation initiatives, built safe and reliable AI-powered platforms and products, and helped organizations adopt generative and agentic AI to improve customer experience, increase engineering productivity, and accelerate business transformation.
As an AWS Certified AI Practitioner, educator, mentor, and founder of an AI-powered EdTech venture, she enjoys helping engineers and technology leaders apply AI to solve real business problems. Through this book, she brings together real-world engineering experience and proven testing practices to introduce automated AI TestOps as a practical discipline for building reliable, safe, and trustworthy AI systems.
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