Kubeflow for Machine Learning: From Lab to Production

by Trevor Grant, Holden Karau, Boris Lublinsky

If you're training a machine learning model but aren't sure how to put it into production, this book will get you there. Kubeflow provides a collection of cloud native tools for different stages of a model's lifecycle, from data exploration, feature preparation, and model training to model serving. This guide helps data scientists build production-grade machine learning implementations with Kubeflow and shows data engineers how to make models scalable and reliable.

Using examples throughout the book, authors Holden Karau, Trevor Grant, Ilan Filonenko, Richard Liu, and Boris Lublinsky explain how to use Kubeflow to train and serve your machine learning models on top of Kubernetes in the cloud or in a development environment on-premises.


Understand Kubeflow's design, core components, and the problems it solves
Learn how to set up Kubeflow on a cloud provider or on an in-house cluster
Train models using Kubeflow with popular tools including scikit-learn, TensorFlow, and Apache Spark
Learn how to add custom stages such as serving and prediction
Keep your model up-to-date with Kubeflow Pipelines
Understand how to validate machine learning pipelines

244 pages, Paperback

Published October 20, 2020

Reviews

[Mike Fowler · Rating 2 out of 5]

A quick overview of Kubeflow but somewhat shallow. A lot of the examples are frustrating to follow unless you wish to get all the code from all the links; likely this book is better as an ebook. Seems a bit rushed too with a lot of assumed knowledge about Kubernetes through the book with explanations appearing later on.

[Thang · Rating 4 out of 5]

The book covers many aspects and Kubeflow features, but on the surface only. A simple introduction to ML concepts like AutoML, NAS.