Machine Learning Production Systems

Foreword

My first big break in AI and machine learning (ML) came about 20 years ago. It was during a time when the internet still felt like a brand new technology. The world was noticing that the power of free communication had drawbacks as well as benefits—with those drawbacks being most notable in the form of email spam. These unwanted messages were clogging up inboxes everywhere with shady offers for pills or scams seeking bank account information.

Email spam was a raging problem because the available spam filters (being based largely on hand-crafted rules and patterns) were ineffective. Spammers would fool these filters with all kinds of tricks, like int3nt!onal mi$$pellings or o t h e r h a c k y m e t h o d s that were hard for a fixed rule to adapt to. As a grad student at the time, I became part of the community of researchers that believed a funny technology called machine learning might be the right solution for this set of problems. I was even lucky enough to create a model that won one of the early benchmark competitions for email spam filtering.

I remember that early model for two reasons. First, it was kind of cool that it worked well by using a simple but very flexible representation—something that we would now call an early precursor to a one-dimensional convolution on strings. Second, I can look back and say with certainty that it would have been an absolute mess to put into a production environment. It had been designed under the pressures of academic research, ...