Navigate the complexities of AI testing with this authoritative guide, tailored for quality engineering professionals eager to enhance their expertise in evaluating AI-infused applications. This book provides an in-depth exploration of the challenges faced in developing AI testing platforms, offering actionable solutions and insights drawn from the author's extensive research and collaborations with industry experts. As AI reshapes industries worldwide, the responsibilities of quality engineers are rapidly evolving. This book equips professionals to adapt to these changes by delivering a clear understanding of AI testing methodologies, the challenges unique to AI systems, and the opportunities they present. The author shares a compelling personal journey from a mainframe developer to a recognized thought leader in AI testing, providing practical advice, real-world examples, and proven strategies to guide readers through this dynamic field. From foundational AI concepts and evaluating probabilistic systems to leveraging generative AI and implementing cutting-edge testing techniques, this book offers a comprehensive roadmap to mastering the quality assurance of AI-driven applications. Whether you're a seasoned professional or new to AI testing, this book delivers the knowledge, tools, and inspiration to succeed in this transformative era. Prepare to stay ahead in the fast-paced world of AI and revolutionize the way you approach quality engineering.
This book covers:
The Evolution of Quality Engineering in the AI Era: Explore how AI is reshaping the field and the critical role quality engineers play in the modern development lifecycle.Core Concepts of AI for Quality Engineers: Gain a solid understanding of AI principles, terminology, and applications, tailored specifically for quality professionals.Testing Probabilistic AI Systems: Learn the key differences between deterministic and probabilistic systems, and how to evaluate reliability, performance, and quality.Evaluating AI System Components: Delve into the quality assessment of machine learning models, data pipelines, and algorithms to identify and address issues effectively.Adapting Traditional QE Practices for AI: Discover how to customize existing quality engineering practices to meet the unique demands of AI-driven solutions, including defining AI-specific metrics and objectives.Harnessing the Power of Generative AI: Explore the transformative potential of generative AI in quality engineering, including innovative test case generation and diversity techniques.AI-based Application Testing and Reporting: Learn how to implement AI-enhanced testing strategies and effectively communicate outcomes to stakeholders.
The concluding chapters present a clear roadmap for becoming a thought leader in AI testing, highlighting the education, skills, and strategies needed to stay ahead in this dynamic domain. Written by a seasoned quality engineering professional who transitioned from mainframe development to AI testing thought leadership, [Book Title] combines years of expertise with actionable advice and real-world case studies. Whether you're just beginning your journey in AI testing or you're a seasoned quality engineer seeking to broaden your skillset, this book offers value for professionals at every level.
