Essential Kubeflow : Engineering ML Workflows on Kubernetes

Josyula, Prashanth/ Arora, Sonika/ Kumar, Anant

Morgan Kaufmann Publishers In（2026/07発売）

• 製本 Paperback:紙装版/ペーパーバック版／ページ数 250 p.
• 言語 ENG
• 商品コード 9780443452543

Full Description

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.

Contents

Part I: Foundation
1. Kubernetes Essentials for ML Engineers
2. Getting Started with Kubeflow

Part II: Building ML Workflows
3. Understanding Kubeflow Pipelines
4. Advanced Pipeline Development
5. Experimentation with Notebooks

Part III: Model Development and Training
6. Training at Scale
7. Hyperparameter Tuning with Katib

Part IV: Model Deployment
8. Serving Models with KServe
9. Production Operations

Part V: Enterprise Implementation
10. Production Best Practices
11. Platform Integration and Ecosystem