Скачать с ютуб Deploying Many Models Efficiently with Ray Serve в хорошем качестве

Deploying Many Models Efficiently with Ray Serve

Serving numerous models is essential today due to diverse business needs and various customized use-cases. However, this raises the challenge of how to efficiently deploy and manage these models while considering both ease of use and cost-effectiveness. This talk aims to provide a comprehensive insight into various patterns of serving many models using Ray Serve. We will delve into how 3 features in Ray Serve - model composition, multi-application, model multiplexing - enable seamless deployment of numerous models while optimizing resource utilization. Takeaways: • Discuss common industry patterns for serving many models. • Learn how to simplify management and enhance performance of many-model serving through Ray Serve's model composition, multi-application, and model multiplexing features. • Deep dive into case studies of Ray Serve users running many-model applications in production. Find the slide deck here: https://drive.google.com/file/d/1R5r_... About Anyscale --- Anyscale is the AI Application Platform for developing, running, and scaling AI. https://www.anyscale.com/ If you're interested in a managed Ray service, check out: https://www.anyscale.com/signup/ About Ray --- Ray is the most popular open source framework for scaling and productionizing AI workloads. From Generative AI and LLMs to computer vision, Ray powers the world’s most ambitious AI workloads. https://docs.ray.io/en/latest/ #llm #machinelearning #ray #deeplearning #distributedsystems #python #genai