Kubeflow for Machine Learning

Preface

We wrote this book for data engineers and data scientists who are building machine learning systems/models they want to move to production. If you’ve ever had the experience of training an excellent model only to ask yourself how to deploy it into production or keep it up to date once it gets there, this is the book for you. We hope this gives you the tools to replace Untitled_5.ipynb with something that works relatively reliably in production.

This book is not intended to serve as your first introduction to machine learning. The next section points to some resources that may be useful if you are just getting started on your machine learning journey.

Our Assumption About You

This book assumes that you either understand how to train models locally, or are working with someone who does. If neither is true, there are many excellent introductory books on machine learning to get you started, including Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 2nd Edition, by Aurélien Géron (O’Reilly).

Our goal is to teach you how to do machine learning in a repeatable way, and how to automate the training and deployment of your models. A serious problem here is that this goal includes a wide range of topics, and it is more than reasonable that you may not be intimately familiar with all of them.

Since we can’t delve deeply into every topic, we would like to provide you a short list of our favorite primers on several of the topics you will see covered here:

• Python ...