KubeCon + CloudNativeCon + Open Source Summit + AI_dev China 2024

As AI/ML models grow in scale and complexity, how to efficiently deploy and manage model service in cloud-native environments has become a significant challenge. This proposal will introduce the Model Service Mesh (MSM), an emerging architectural paradigm designed specifically for large-scale AI model service deployment and management, to address the challenge. This new paradigm focuses on: 1. How to build a highly scalable and reliable model delivery system and the key features include dynamic model service routing, unified management for multi-models within single endpoint, an optimized caching layer, and cache-aware scheduling,etc. 2. How to leverage the MSM to optimize AI models service in lifecycle management, resource utilization improvement, security enhancement, and observability and resilience insurance. In essence, this architecture ensures a scalable, secure, and efficient model service in cloud native environment.

随着人工智能/机器学习模型规模和复杂性的增长，如何在云原生环境中高效部署和管理模型服务已成为一个重大挑战。本提案将介绍模型服务网格（MSM），这是一种专门为大规模人工智能模型服务部署和管理而设计的新兴架构范式，旨在解决这一挑战。这种新范式关注以下几点： 1. 如何构建一个高度可扩展和可靠的模型交付系统，关键特性包括动态模型服务路由、单个端点内多模型的统一管理、优化缓存层和缓存感知调度等。 2. 如何利用MSM优化人工智能模型服务的生命周期管理、资源利用率改善、安全增强以及可观察性和弹性保障。 总的来说，这种架构确保了在云原生环境中可扩展、安全和高效的模型服务。