Machine Learning Logistics

Chapter 1. Why Model Management?

90% of the effort in successful machine learning is not about the algorithm or the model or the learning. It’s about logistics.

Why is model management an issue for machine learning, and what do you need to know in order to do it successfully?

In this book, we explore the logistics of machine learning, lumping various aspects of successful logistics under the topic “model management.” This process must deal with data flow and handle multiple models as well as collect and analyze metrics throughout the life cycle of models. Model management is not the exciting part of machine learning—the cool new algorithms and machine learning tools—but it is the part that unless it is done well is most likely to cause you to fail. Model management is an essential, ubiquitous and critical need across all types of machine learning and deep learning projects. We describe what’s involved, what can make a difference to your success, and propose a design—the rendezvous architecture—that makes it much easier for you to handle logistics for a whole range of machine learning use cases.

The increasing need to deal with machine learning logistics is a natural outgrowth of the big data movement, especially as machine learning provides a powerful way to meet the huge and, until recently, largely unmet demand for ways to extract value from data at scale. Machine learning is becoming a mainstream activity for a large and growing number of businesses and research organizations. ...