Prashanth Josyula – 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

Istio is a powerful service mesh technology that enhances the security, observability, and traffic management of microservices within cloud-native architectures. As organizations increasingly adopt microservices, managing the complexity of service interactions becomes critical. This book is designed to help professionals navigate these complexities by providing a deep understanding of Istio, empowering them to leverage their full potential in building resilient, scalable, and secure service infrastructures.Practical Istio offers a comprehensive journey through the service mesh, beginning with an introduction to its architecture and fundamental concepts. Readers will delve into advanced topics such as traffic management, security enforcement, and observability, gaining insights into how to implement and optimize Istio in real-world scenarios. Each chapter is crafted to provide practical guidance, including detailed explanations of Istio’s key components, like 'Istiod' and 'Envoy', hands-on examples for managing production grade deployments, and strategies for integrating Istio with CI/CD pipelines. The book also covers emerging trends and future directions of service mesh technology, ensuring readers stay ahead of the curve.By the end of this book, readers will be equipped with the knowledge and skills needed to implement Istio in production environments. Whether you’re a DevOps engineer, a cloud architect, or a software developer, this book will elevate your understanding of service mesh technology, enabling you to build robust, secure, and efficient microservices architectures with Istio at the core.You Will Learn:●        Istio’s architecture and core components enable you to effectively manage and secure microservices in a cloud-native environment.●        Practical skills in traffic management, security, and observability to optimize service performance and reliability●        To explore more advanced techniques for production grade deployments and extending Istio with custom plugins and adapters●         Real-world case studies and examples, give you the practical knowledge to implement, troubleshoot, and optimize Istio in production environmentsThis Book is For:Software Developers and Engineers, Researchers, IT Architects, Cloud Engineers, Site Reliability Engineers (SREs), Security Engineers, MLOps and AIOps as well as DevOps Managers and Product Owners