商品簡介
Essential Kubeflow: Engineering ML Workflows on Kubernetes equips readers with the tools to transform ML workflows from experimental notebooks to production-ready platforms with this comprehensive guide to Kubeflow, one of the most widely adopted open source MLOps platforms used to automate ML workloads. Whether you're a Machine Learning engineer looking to operationalize models, a platform engineer diving into ML infrastructure, or a technical leader architecting ML systems, this book provides practical solutions for real-world challenges. Through hands-on examples and production-tested patterns, readers will master essential skills for building enterprise-grade Machine Learning platforms: architecting production systems on Kubernetes, designing end-to-end ML pipelines, implementing robust model serving, scaling workloads efficiently, managing multi-user environments, deploying automated MLOps workflows, and integrating with existing ML tools. By the end of this book, readers will have the expertise to build and maintain scalable ML platforms that can handle the demands of modern enterprise AI initiatives.
