Kubeflow Operations And Workflow Engineering
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Kubeflow Operations and Workflow Engineering
"Kubeflow Operations and Workflow Engineering" Unlock the full potential of machine learning at scale with "Kubeflow Operations and Workflow Engineering". This comprehensive guide provides a deep dive into the architecture, pipeline design, deployment patterns, and operational best practices behind Kubeflow—an industry-standard platform for orchestrating complex AI workflows on Kubernetes. Readers will explore Kubeflow’s modular microservices, core capabilities, and advanced orchestration paradigms, empowering them to design, deploy, and manage reliable machine learning solutions for enterprise environments. The book takes practitioners from foundational concepts through to specialized topics such as pipeline engineering, production-grade deployment, workflow scheduling, and resource optimization. Through detailed explorations of topics like component interoperability, state management, dynamic pipelines, distributed model training, and integration patterns, readers will learn proven methods to build robust, scalable, and secure MLOps infrastructures. Chapters on security, compliance, observability, and resilience address the demands of modern production environments and highly regulated industries, with guidance on access management, logging, policy enforcement, and high-availability design. Moving beyond the fundamentals, real-world case studies and emerging trends illuminate how leading organizations operationalize Kubeflow at scale, navigate hybrid and edge deployments, and integrate with modern tools and frameworks. Whether implementing federated learning, event-driven pipelines, or large language models, this book equips AI engineers, architects, and DevOps professionals with the practical knowledge to innovate and lead in the evolving MLOps landscape, leveraging Kubeflow as a strategic foundation for enterprise machine learning success.
Kubeflow Operations Guide
Author: Josh Patterson
language: en
Publisher: "O'Reilly Media, Inc."
Release Date: 2020-12-04
Building models is a small part of the story when it comes to deploying machine learning applications. The entire process involves developing, orchestrating, deploying, and running scalable and portable machine learning workloads--a process Kubeflow makes much easier. This practical book shows data scientists, data engineers, and platform architects how to plan and execute a Kubeflow project to make their Kubernetes workflows portable and scalable. Authors Josh Patterson, Michael Katzenellenbogen, and Austin Harris demonstrate how this open source platform orchestrates workflows by managing machine learning pipelines. You'll learn how to plan and execute a Kubeflow platform that can support workflows from on-premises to cloud providers including Google, Amazon, and Microsoft. Dive into Kubeflow architecture and learn best practices for using the platform Understand the process of planning your Kubeflow deployment Install Kubeflow on an existing on-premises Kubernetes cluster Deploy Kubeflow on Google Cloud Platform step-by-step from the command line Use the managed Amazon Elastic Kubernetes Service (EKS) to deploy Kubeflow on AWS Deploy and manage Kubeflow across a network of Azure cloud data centers around the world Use KFServing to develop and deploy machine learning models
Official Google Cloud Certified Professional Machine Learning Engineer Study Guide
Expert, guidance for the Google Cloud Machine Learning certification exam In Google Cloud Certified Professional Machine Learning Study Guide, a team of accomplished artificial intelligence (AI) and machine learning (ML) specialists delivers an expert roadmap to AI and ML on the Google Cloud Platform based on new exam curriculum. With Sybex, you’ll prepare faster and smarter for the Google Cloud Certified Professional Machine Learning Engineer exam and get ready to hit the ground running on your first day at your new job as an ML engineer. The book walks readers through the machine learning process from start to finish, starting with data, feature engineering, model training, and deployment on Google Cloud. It also discusses best practices on when to pick a custom model vs AutoML or pretrained models with Vertex AI platform. All technologies such as Tensorflow, Kubeflow, and Vertex AI are presented by way of real-world scenarios to help you apply the theory to practical examples and show you how IT professionals design, build, and operate secure ML cloud environments. The book also shows you how to: Frame ML problems and architect ML solutions from scratch Banish test anxiety by verifying and checking your progress with built-in self-assessments and other practical tools Use the Sybex online practice environment, complete with practice questions and explanations, a glossary, objective maps, and flash cards A can’t-miss resource for everyone preparing for the Google Cloud Certified Professional Machine Learning certification exam, or for a new career in ML powered by the Google Cloud Platform, this Sybex Study Guide has everything you need to take the next step in your career.