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Explore the application of chaos engineering principles to machine learning and generative AI systems in this 59-minute conference talk. Discover how to systematically introduce controlled failures and disruptions to test the resilience and reliability of ML models, from traditional machine learning algorithms to modern large language models. Learn practical approaches for identifying weaknesses in ML pipelines, understanding failure modes in AI systems, and building more robust machine learning infrastructure. Gain insights into testing methodologies that can help ensure your ML and GenAI models perform reliably under unexpected conditions and stress scenarios.
