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Explore a comprehensive 36-minute conference talk that delves into the critical security challenges of deploying machine learning and large language model operations in enterprise environments. Learn from platform architects Sai Gunaranjan and Kyler Middleton as they share practical insights on onboarding MLops and LLMOps tools while maintaining robust security across multi-cloud infrastructures. Discover reference architectures specifically designed for securing ML and LLM workloads in AWS and Azure environments, with particular focus on the unique challenges faced in highly regulated industries like healthcare. Examine native security controls, access patterns for sensitive data, and best practices for protecting AI workloads from unauthorized models and insecure data pipelines. Gain actionable strategies for designing secure AI/ML environments that balance performance requirements with compliance needs, drawing from real-world experiences of platform architects navigating the rapidly evolving landscape of enterprise AI adoption. Understand how to effectively migrate applications to the cloud while ensuring security and availability are maintained throughout the ML and LLM deployment lifecycle.
