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As AI adoption accelerates, managing and scaling AI workloads securely has never been more critical. In this session, you’ll discover how the GenAI gateway capabilities in Azure API Management are purpose-built to manage machine learning models via APIs. Experience a unified control plane designed for seamless integration and governance. \n\nExplore how to address the challenges of scalability, security, and resource allocation while leveraging large language models (LLMs) for your development needs. Engage in dynamic discussions on how to enforce token limits, enhance performance with semantic caching, and ensure enterprise-grade security and compliance across your environments. This session empowers your teams to innovate confidently and with minimal overhead. \n\nWhat You’ll Learn: \n\nEfficiently Scaling AI Workloads: Uncover how to leverage the GenAI gateway capabilities in Azure API Management to effectively manage AI workloads, facilitating growth and adaptability. \n\nImplementing Token Management and Traffic Shaping: Learn the intricacies of enforcing token management, traffic shaping, and semantic caching for AI models, optimizing resource utilization. \n\nEnhancing Security and Governance: Explore robust strategies to enhance security and governance across AI APIs, ensuring your organization adheres to compliance standards while fostering innovation.

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