Foreword

Today’s data scientists and software engineers are spoiled for choice when looking for tools to build machine learning systems. They have a range of new technologies that make it easier than ever to build entire machine learning systems. Considering where we—the machine learning community—started, it’s exciting to see a book that explores how powerful and approachable the current technologies are.

To better understand how we got here, I’d like to share a bit of my own story. They tell me I’m a data scientist, but I think I’m only here by accident. I began as a software person and grew up on Java 1.3 and EJB. I left the software-engineer role at Google a decade ago, although I dabbled in open source and created a recommender system that went on to be part of Apache Mahout in 2009. Its goal was to implement machine learning algorithms on the then-new Apache Hadoop MapReduce framework. The engineering parts were familiar—MapReduce came from Google, after all. The machine learning was new and exciting, but the tools were lacking.

Not knowing any better, and with no formal background in ML, I tried to help build ML at scale. In theory, this was going to open an era of better ML, because more data generally means better models. ML just needed tooling rebuilt on the nascent distributed computing platforms like Hadoop.