As organizations increasingly deploy AI and machine learning systems into production, testing practices built for static, rule-based software are no longer sufficient. Unlike traditional applications, AI systems learn from data, change behavior over time, and are sensitive to data drift, bias, and feedback loops, making defects harder to detect with conventional test cases. This session presents a practical, experience-driven approach to testing AI systems across the full lifecycle, from model development to live deployment. Drawing on real-world implementations and applied research, the...
Surya Narayana Reddy Chintacunta
Data & AI Engineer
Independent Researcher
Surya Narayana Reddy Chintacunta is a data and AI engineer and independent researcher specializing in building and validating scalable machine learning systems in production environments. His work focuses on bridging the gap between AI research and real-world deployment, with particular emphasis on data-centric design, continuous validation, and system reliability. Surya has published peer-reviewed research on AI-driven optimization, real-time feature engineering, and adaptive machine learning systems, and he actively serves as a peer reviewer for journals and conferences in software engineering and applied AI. With experience collaborating across engineering, data, and product teams, he brings a practical perspective on testing and maintaining AI systems that evolve over time under real-world constraints.