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One of the biggest challenges practitioners encounter in their Machine Learning (ML) and large language models (LLMs) journey is scaling up their training and inference workloads. Ray is a unified framework for scalable AI and scalable Python which has been a gamechanger for the ML ecosystem, and it’s been used to train the largest language models. The KubeRay operator enables native support for Ray applications on Kubernetes (K8s) infrastructure. Many leading AI organizations are standardizing their ML infrastructure by using KubeRay to deploy Ray on K8s. In this talk, we will share how KubeRay can accelerate your AI team. You will walk away with: - insights about the benefits of Ray and K8s to ML/AI platforms - guidance to build a performant and cost-effective platform with OSS tools, like AI leaders such as Uber, Shopify, and OpenAI - solutions for scaling your ML and LLM workloads with Ray Serve on K8s - how KubeRay integrates Ray into your existing K8s architecture
Archit Kulkarni is a software engineer at Anyscale working on Ray and KubeRay. Before coming to Anyscale, Archit completed his PhD in mathematics at UC Berkeley.
Winston Chiang is currently the product lead for AI/ML on Google Kubernetes Engine. He has led cloud Machine Learning Platforms at both Google and Amazon. Winston completed his PhD in System Design Theory and MS in Computer Science (AI Robotics). In his extra time, he is the personal... Read More →
