Much like Software Engineering followed Computer Science, as our world increasingly depends on machine learning systems, we argue that the time is right for ML Engineering to come of age. Our paper on ML Engineering is now available on arXiv. It covers a decade of production ML experience, illustrated by two successive end-to-end (E2E) ML platforms at Alphabet: Sibyl and TensorFlow Extended (TFX). I hope you will find it useful and that sharing our experience will help you advance your own ML systems.
Humbled to have played a small part in advancing both of these platforms working together with Konstantinos (Gus) Katsiapis, Abhijit Karmarkar, Ahmet Altay, Aleksandr Zaks, Alkis Polyzotis, Anusha Ramesh, Ben Mathes, Gautam Vasudevan, Irene Giannoumis, Jiri Simsa, Justin Hong, Mitch Trott, Noé Lutz, Pavel Dournov, Robert Crowe, Sarah Sirajuddin, Tris Warkentin, Zhitao Li and many other current and former Googlers acknowledged in this article.