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
Understand the differences between Kubeflow on different cluster types
Train models using Kubeflow with popular tools including Scikit-learn, TensorFlow, and Apache Spark
Keep your model up to date with Kubeflow Pipelines
Understand how to capture model training metadata
Explore how to extend Kubeflow with additional open source tools
Use hyperparameter tuning for training
Learn how to serve your model in production