Скачать с ютуб How Spotify Built a Robust Ray Platform with a Frictionless Developer Experience в хорошем качестве

How Spotify Built a Robust Ray Platform with a Frictionless Developer Experience

What do you need to think about if you want to build a centralized Ray platform for thousands of users of diverse backgrounds and use cases? Our talk shares what Spotify's ML platform team has learned and solved over the past year. We created a seamless developer experience by making it easy to not only get computational resources but also start coding on it. We enhanced our platform's reliability, scalability, performance, and cost-efficiency. We also leveraged the Ray ecosystem for ML development to solve real business problems. These efforts have led to broader adoption of Ray in ML applications at Spotify. We describe the goals and design decisions of our managed Ray platform, our focus on a frictionless developer experience for ML practitioners, and how Ray has accelerated various ML applications. We hope our stories and learnings will inspire and help other members of the community on their Ray journey. Find the slide deck here: https://drive.google.com/file/d/1kI67... 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