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
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
