Managing machines

Machine learning (ML) drove massive growth at consumer internet companies over the last decade, enabled by open software, datasets, and AI research. For many problems, ML will produce better, faster, and more repeatable decisions at scale. Unfortunately, building and maintaining these systems is difficult and expensive. Pete Skomoroch explores what you need to produce better ML results.

Machine learning drove massive growth at consumer internet companies over the last decade, and this was enabled by open software, datasets, and AI research. For many problems, machine learning will produce better, faster, and more repeatable decisions at scale. Unfortunately, building and maintaining these systems is still extremely difficult and expensive. As more machine learning software moves to production, many of our traditional tools and best practices in software development will change. Pete Skomoroch walks you through what you need to know as we shift from a world of deterministic programs to systems that give unpredictable results on ever-changing training data. To navigate this world powered by nondeterministic data-dependent programs, we’ll also need a new development stack to help us write, test, deploy, and monitor machine learning software.