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Join this 58-minute tech talk exploring the application of chaos engineering principles to machine learning and generative AI workloads. Discover how deliberate failure injection can strengthen system resilience in AI environments, examine real-world experiments targeting AI model vulnerabilities, and understand why proactive failure testing has become essential in today's rapidly evolving AI landscape. Learn about the origins of chaos engineering through Netflix's Chaos Monkey, explore the unique challenges of testing non-deterministic large language models, and gain insights into organizational adoption strategies. The discussion covers fundamental chaos engineering concepts, practical implementation approaches for ML systems, testing methodologies for AI model resilience, and addresses the specific complexities introduced by the non-deterministic nature of modern AI systems, making it valuable for engineers, data scientists, and technology leaders working with AI infrastructure.
