Technology · Ebook

Kubeflow: Machine Learning on Kubernetes

by Shriira Press

Kubeflow is an open-source, end-to-end machine learning platform on Kubernetes — providing the full ML lifecycle (notebooks, pipelines, distributed training, hyperparameter tuning, serving) as integrated components. This free book teaches it from the ground up: the ML platform problem and what Kubeflow is, the ML lifecycle and MLOps, Kubeflow's architecture (the integrated components), notebooks (interactive experimentation), Kubeflow Pipelines (the orchestration backbone), distributed training (the Training Operator), hyperparameter tuning (Katib), model serving (KServe), metadata/multi-tenancy/the platform (tracking, isolation, operations), and using Kubeflow in practice (adoption, the ecosystem, best practices). Ten focused chapters with clear diagrams that demystify MLOps on Kubernetes — turning ad-hoc machine learning into a disciplined, reproducible, automated, scalable end-to-end platform.

Contents

1. 1Preface
2. 2Chapter 1 — What Kubeflow Is
3. 3Chapter 2 — The ML Lifecycle and MLOps
4. 4Chapter 3 — Kubeflow's Architecture
5. 5Chapter 4 — Notebooks: Interactive Experimentation
6. 6Chapter 5 — Kubeflow Pipelines
7. 7Chapter 6 — Distributed Training
8. 8Chapter 7 — Hyperparameter Tuning with Katib
9. 9Chapter 8 — Model Serving with KServe
10. 10Chapter 9 — Metadata, Multi-Tenancy, and the Platform
11. 11Chapter 10 — Using Kubeflow in Practice