Essential Kubeflow
Engineering ML Workflows on Kubernetes
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Product details:
- Publisher Elsevier Science
- Date of Publication 1 July 2026
- ISBN 9780443452543
- Binding Paperback
- No. of pages250 pages
- Size 235x191 mm
- Weight 450 g
- Language English 700
Categories
Long 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.
Table of 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
