Building Robust AI and ML Systems: A Quality Engineering Perspective

AI and machine learning have made exciting advancements in the past few years. From chatbots that seamlessly assist with customer inquiries to personal assistants that boost productivity, these AI tools have demonstrated their transformative potential. Organizations are increasingly adopting AI-driven strategies and innovations to stay competitive. However, as they develop and deploy these cutting-edge applications, they encounter significant quality, scalability, and reliability challenges. This article explores the quality engineering landscape for ML and AI applications, providing an in-depth look at essential stages of the ML lifecycle. We’ll cover data acquisition, transformation, storage, pipelines, governance, infrastructure, ML model performance, and the software development lifecycle. Packed with practical insights and proven testing techniques, this guide is designed to help professionals confidently and efficiently launch robust ML solutions. Data Acquisition The success ... Read full article.