Kubeflow for Machine Learning: From Lab to Production
Open Source Engineer at Netflix
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.
BOOK EPISODE
Kubeflow for Machine Learning
Machine Learning has been declared dead several times but that’s far from true. Join Adi Polak, vice president of developer experience at Treeverse, and Holden Karau, open source engineer at Netflix, in their conversation about Kubeflow and how it provides better tooling in the ML space. The discussion touches on Holden’s book “Kubeflow for Machine Learning” and expands to cover the worlds of Ray and Dask.
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