AI Model Distribution Challenges and Best Practices in Cloud-Native Infrastructure

Explore the critical challenges and solutions for distributing AI models at scale in cloud-native environments through this 33-minute conference talk featuring industry experts from Ant Group, Alibaba Cloud, and Kuaishou. Dive deep into technical and operational strategies for deploying machine learning models efficiently across distributed infrastructure, focusing on optimizing model storage, transfer mechanisms, and maintaining consistency across clusters and regions. Learn how Kubernetes-native workflows can automate model distribution while minimizing latency and bandwidth costs, and discover approaches for handling massive models ranging from hundreds of gigabytes to terabytes. Examine the complexities of distributed inference architectures, including prefilling-decoding patterns, and understand model update strategies in reinforcement learning post-training scenarios. Gain insights into leveraging standards like OCI artifacts and specialized registries for streamlined versioned model delivery, as panelists share real-world experiences and best practices from leading technology companies working at the forefront of scalable AI/ML deployment.