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.
1. Title of Book - p. i 2. Cover Image - p. i 3. Title Page - p. ii 4. Table of Contents - p. iii 5. Copyright - p. iv 6. Dedication - p. v 7. About the authors - p. xiii 8. Preface - p. xv 9. Acknowledgments - p. xvii 10. Part I: Foundation - p. P1 11. Chapter 1 Kubernetes essentials for machine learning engineers - p. 3 12. Chapter 2 Getting started with Kubeflow - p. 33 13. Part II: Building machine learning workflows - p. P2 14. Chapter 3 Understanding kubeflow pipelines - p. 61 15. Chapter 4 Advanced pipeline development - p. 103 16. Chapter 5 Experimentation with notebooks - p. 135 17. Part III: Model development and training - p. P3 18. Chapter 6 Training at scale - p. 161 19. Chapter 7 Hyperparameter tuning with Katib - p. 207 20. Part IV: Model deployment and production operations - p. P4 21. Chapter 8 KServe fundamentals - p. 247 22. Chapter 9 Advanced KServe - p. 287 23. Chapter 10 Production operations and integrations - p. 321 24. Index - p. 357
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.
1. Title of Book - p. i 2. Cover Image - p. i 3. Title Page - p. ii 4. Table of Contents - p. iii 5. Copyright - p. iv 6. Dedication - p. v 7. About the authors - p. xiii 8. Preface - p. xv 9. Acknowledgments - p. xvii 10. Part I: Foundation - p. P1 11. Chapter 1 Kubernetes essentials for machine learning engineers - p. 3 12. Chapter 2 Getting started with Kubeflow - p. 33 13. Part II: Building machine learning workflows - p. P2 14. Chapter 3 Understanding kubeflow pipelines - p. 61 15. Chapter 4 Advanced pipeline development - p. 103 16. Chapter 5 Experimentation with notebooks - p. 135 17. Part III: Model development and training - p. P3 18. Chapter 6 Training at scale - p. 161 19. Chapter 7 Hyperparameter tuning with Katib - p. 207 20. Part IV: Model deployment and production operations - p. P4 21. Chapter 8 KServe fundamentals - p. 247 22. Chapter 9 Advanced KServe - p. 287 23. Chapter 10 Production operations and integrations - p. 321 24. Index - p. 357
