Responsible AI Testing
Idioma: inglés
Editorial: Amazon Digital Services LLC - Kdp, 2025
- Tapa blanda
- Nuevo
Librería: PBShop.store US, Wood Dale, IL, Estados Unidos de AmericaPBShop.store US
Vendedor de IberLibro desde 7 de abril de 2005
Condición: Nuevo
EUR 25,52
Cantidad disponible: Más de 20 disponibles
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N° de ref. del artículo L0-9798298574709
- Título
- Responsible AI Testing
- Autor
- RAJPUT, ABHAY
- Editorial
- Amazon Digital Services LLC - Kdp
- Año de publicación
- 2025
- Estado
- New
- Encuadernación
- PAP
- Idioma
- inglés
- ISBN 13
- 9798298574709
- Peso del artículo
- 590 gramos
✨ Responsible AI Testing by Abhay Rajput is your essential guide to mastering AI Model Evaluation and AI Quality Assurance—whether you’re a beginner, professional, or industry leader. This book is not just about understanding AI; it’s about implementing practical, ethical, and reliable evaluation methods that ensure your AI systems perform with accuracy, fairness, and transparency.
📌 If you’ve ever asked:
- How do I measure AI accuracy, precision, recall, or F1 score?
- How do I build fairness, trust, and explainability into my models?
- How can I ensure AI is reliable, ethical, and future-ready?
👉 This book has the answers.
🌍 The Need
AI is reshaping industries, but without trustworthy evaluation, risks grow. Models can be biased, inaccurate, or unreliable if not tested properly. From AI performance metrics to bias testing, from explainable AI methods to AI governance standards, this book covers every aspect of responsible AI evaluation. With real-world examples, step-by-step guides, and easy-to-follow frameworks, you’ll learn how to build systems that are not just smart, but also ethical, transparent, and accountable.
💡 The Value
Inside, you’ll explore:
✅ AI accuracy testing: Learn how to measure accuracy, precision, recall, and F1 score effectively.
✅ Confusion matrix mastery: Interpret results to improve real-world AI deployments.
✅ AI fairness evaluation: Spot and reduce bias with proven frameworks.
✅ Trustworthy AI systems: Develop models that inspire confidence in users and stakeholders.
✅ Reliable AI delivery: Practical guidance for deployment, monitoring, scaling, and post-deployment maintenance.
✅ AI accountability frameworks: Discover global standards, governance practices, and ethical implementation.
✅ Future-ready insights: Explore emerging trends in AI quality assurance and social impact evaluation.
Every chapter blends awareness + implementation so you not only understand concepts but also apply them with confidence in your projects, audits, and assessments.
🎯 Additional Benefits
🔹 Written in clear, layman-friendly English—perfect for beginners yet detailed enough for experts.
🔹 Covers metrics that matter—from accuracy to explainability, ensuring your models are trustworthy and transparent.
🔹 Provides practical steps, sample reports, assessment methods, and audit-ready guidelines.
🔹 Rich with real-world examples across industries—bridging theory and practice seamlessly.
🔹 Includes special services offered by the author (details inside)—ideal for professionals, teams, and organizations seeking deeper guidance.
🔑 Why This Book Stands Out
Unlike generic AI books, Responsible AI Testing is built on real industry knowledge, combining AI model evaluation, ethical AI practices, and reliable delivery strategies. With 250+ pages of structured insights across 7 detailed chapters, it is both a guidebook and a practical playbook for ensuring trustworthy AI implementation.
Whether you’re working on AI monitoring systems, scaling AI responsibly, human-AI collaboration, or social impact evaluations—this book equips you with knowledge, tools, and confidence.
📖 Get ready to master AI model evaluation, ensure accuracy with ethics, and deliver AI responsibly.
👉 Your journey to building transparent, fair, and reliable AI starts here.
🌟 Buy your copy today and become a leader in Responsible AI Testing! 🌟
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PBShop.store US
Wood Dale, IL, Estados Unidos de America
Vendedor de IberLibro desde 7 de abril de 2005
Tarifas de envío en Estados Unidos de America
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|---|---|---|
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