Accelerate Machine Learning with a Unified Analytics Architecture

Chapter 1. Applied Machine Learning and Why It Fails

There are many reasons for the contrast between potential and actual applications of ML in industry, but at its core, the failure is due to the unique challenges of operationalizing ML, commonly known as MLOps. Although companies across the globe have standardized the practice of DevOps, the workflow and automation of putting code into production systems, MLOps presents a uniquely different set of problems.

In the practice of DevOps, the code is the product; what is written by the engineer defines precisely how the system acts and what the customer sees. The code is easy to track, manage, update, and correct. But MLOps isn’t as straightforward; code is only half of the solution, and in many cases, much less than half. MLOps requires the seamless cooperation, coordination, and trust of code and data, a problem that has not been fully tackled and standardized by the industry.

When an ML model is put into production, teams must access not only the code that created it, and the model artifact itself, but the data it was trained on, as well as the data it begins making predictions on post-deployment. Because a model is merely an interpretation of a set of data points, any change in that original dataset, by definition, is a new model. And as the model makes predictions in the real world, any discrepancies between the distributions of data the model trained on and data it predicted on (commonly referred to as data drift) will cause ...