Unlock the potential of custom large language models (LLMs) with Azure AI's powerful infrastructure. This session explores Azure Machine Learning and Azure AI infrastructure, including ND H100 and H200 series. Learn how to leverage distributed training to enhance scalability, performance, and efficiency. We'll cover practical techniques for optimizing LLM training workflows, ensuring you can harness the full potential of Azure AI to accelerate your AI projects.
John Lee
Principal Lead, Azure AI Infrastructure and Platforms
John Lee is the AI Platforms and Infrastructure Principal Lead at Microsoft, where he leads a team of Artificial Intelligence (AI) Product Managers and Technical Program Managers. In this role, John and his team are responsible for defining, shaping, and delivering all current and future AI VM product offerings in Azure, supporting some of the most strategic and important customers, including OpenAI.Before joining Microsoft, John served as the General Manager of HPC Business and Platform Strategy at Intel Corporation. Prior to that, he was the Vice President of Cluster Products at Cray, which was later acquired by HPE.
Ben Levine
Software Engineer
I am a software engineer working in foundational AI at LinkedIn. I have worked in applied machine learning research for 10 years, with a focus on infrastructure, deep learning frameworks and distributed systems.
Yogi Pandey
Senior Program Manager
Yogendra (Yogi) Pandey is a Senior Program Manager with Microsoft AI Platform - AI Core Product team. He works on building and operationalizing product components for large language model (LLM) inference at scale.
Alejandra Rico
Sr. Product Manager
Alejandra Rico is a Senior Product Manager for large-scale training in the AI Platform team at Microsoft, with over eight years of experience in artificial intelligence and cloud solutions. She specializes in developing and optimizing large-scale AI training platforms that power complex AI models across diverse industries. Alejandra’s work focuses on integrating advanced frameworks, enhancing GPU utilization, and refining user experiences to drive innovation and scalability. Her strong background in cloud technologies and software engineering enables her to deliver robust, high-performance AI solutions that meet evolving customer needs. Alejandra holds a Master’s degree in Computer Science - Interactive Intelligence from Georgia Tech, combining technical proficiency with strategic vision to lead cross-functional teams and push the boundaries of AI technology.