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As the demand for scalable AI/ML grows, efficiently distributing AI models in cloud-native infrastructure has become a pivotal challenge for enterprises. The panel dives into the technical and operational strategies for deploying models at scale -- from optimizing model storage and transfer to ensuring consistency across clusters and regions. Experts from different companies and CNCF projects will debate critical questions like: How can Kubernetes-native workflows automate and accelerate model distribution while minimizing latency and bandwidth costs? How to efficiently distribute huge models sizing hundreds of GBs or TBs? What are the challenges proposed by distributed inference and the prefilling-decoding architecture? How are models updated in the reinforcement learning post-training paradigm? What role do standards like OCI artifacts or specialized registries play in streamlining versioned model delivery?
Software Engineering from Kuaishou, previously worked in the Kubernetes ecosystem and container-related technologies. Currently, he is focused on optimizing the inference performance of large language models.
Xiaoya Xia is a member of the Ant Group OSPO, where she focuses on catalyzing open source success through data-driven insights. Before joining Ant Group, Xiaoya was a PhD at East China Normal University (ECNU), where she concentrated on research into open source ecosystem sustain... Read More →
Wenbo Qi is a software engineer at Ant Group working on Dragonfly. He is a maintainer of the Dragonfly. He hopes to do some positive contributions to open source software and believe that fear springs from ignorance.