Anant Kumar – författare

Essential Kubeflow: Engineering ML Workflows on Kubernetes provides the tools needed to transform ML workflows from experimental notebooks to production-ready platforms. Through hands-on examples and production-tested patterns, readers will master essential skills for building enterprise-grade Machine Learning platforms, including architecting production systems on Kubernetes, designing end-to-end ML pipelines, implementing robust model serving, efficiently scaling workloads, managing multi-user environments, deploying automated MLOps workflows, and integrating with existing ML tools. 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 solutions for real-world challenges.With this comprehensive guide to Kubeflow, a widely adopted open source MLOps platforms for automating ML workloads, readers will have the expertise to build and maintain scalable ML platforms that can handle the demands of modern enterprise AI initiatives.Provides readers with a comprehensive, step-by-step guide to building reliable ML pipelines with automated workflows, testing, and deployment using Kubeflow's pipeline componentsIncludes clear strategies for monitoring ML workloads, managing resources, handling multi-user environments, and maintaining production platforms at scalePresents proven solutions and architectural patterns drawn from actual production deployments, showing readers how to avoid common pitfalls and accelerate ML initiatives

Essential Kubeflow: Engineering ML Workflows on Kubernetes provides the tools needed to transform ML workflows from experimental notebooks to production-ready platforms. Through hands-on examples and production-tested patterns, readers will master essential skills for building enterprise-grade Machine Learning platforms, including architecting production systems on Kubernetes, designing end-to-end ML pipelines, implementing robust model serving, efficiently scaling workloads, managing multi-user environments, deploying automated MLOps workflows, and integrating with existing ML tools. 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 solutions for real-world challenges.With this comprehensive guide to Kubeflow, a widely adopted open source MLOps platforms for automating ML workloads, readers will have the expertise to build and maintain scalable ML platforms that can handle the demands of modern enterprise AI initiatives. - Provides readers with a comprehensive, step-by-step guide to building reliable ML pipelines with automated workflows, testing, and deployment using Kubeflow's pipeline components- Includes clear strategies for monitoring ML workloads, managing resources, handling multi-user environments, and maintaining production platforms at scale- Presents proven solutions and architectural patterns drawn from actual production deployments, showing readers how to avoid common pitfalls and accelerate ML initiatives