Kubeflow for Machine Learning

Foreword

Occasionally over the years people will ask me what skills are most in demand in tech. Ten years ago I would tell them to study machine learning, which can scale automated decision making in ways previously impossible. However, these days I have a different answer: machine learning engineering.

Even just a few years ago if you knew machine learning and started at an organization, you would likely walk in the door as the only person with that skill set, allowing you to have an outsized impact. However, a side effect of the proliferation of books, tutorials, e-courses, and boot camps (some of which I have written myself) teaching an entire generation of technologists the skills required is that now machine learning is being used across tens of thousands of companies and organizations.

These days a more likely scenario is that, walking into your new job, you find an organization using machine learning locally but unable to deploy it to production or able to deploy models but unable to manage them effectively. In this setting, the most valuable skill is not being able to train a model, but rather to manage all those models and deploy them in ways that maximize their impact.

In this volume, Trevor Grant, Holden Karau, Boris Lublinsky, Richard Liu, and Ilan Filonenko have put together what I believe is an important cornerstone in the education of data scientists and machine learning engineers. For the foreseeable future the open source Kubeflow project will be a common tool ...