Let’s look at three distinct disciplines—DevOps, MLOps and DataOps. In 2011, Marc Andreessen famously proclaimed that software was “eating the world.” A little more than a decade later, it’s all but impossible to argue with his premonition as software has embedded itself into virtually every industry.
But now another shift is underway and AI is gobbling up software as we know it. Our emails can finish our sentences for us, connected cars are helping us stay safer on the road and predictive technology is helping minimize supply chain disruptions caused by COVID-19. In the near future, almost every piece of software we interact with will have intelligence built-in, and as AI gains acceptance and new use cases are rolled out, applications will only continue to become more intelligent. The widespread move toward ML-enabled software has the potential to be as transformative and far-reaching as what Andreessen predicted.
But innovation is rarely easy or simple—and intelligent applications are no exception. While conventional software involves one thing—code—and not much else, intelligent software relies on a complex relationship between three interconnected variables or legs of a three-legged stool.
ATfree-legged Stool: DevOps, MLOps and DataOps
Model: The one or more AI/ML models (eg, linear regressions, rules, deep neural networks, etc.) are trained to recognize patterns in data and make decisions are what makes an application “intelligent.”
Data: Most often, a model is trained on historical data and programmed to emulate it. As a result, an application’s behavior depends heavily on the data—the raw input data, labels and features, as well as the new data to which the model is applied and ground-truth for this data.
Code: Code is the language an application uses to function. When used in the context of intelligent applications, the code pillar might refer to business logic, calls to models, receiving outputs, decision making and calls to other data systems.
A Real-World Example
To see how these pillars play out in real life, consider the example of a perception system for a self-driving car. Such systems use data (provided by offline sources and real-time cameras and sensors), models and code to determine myriad ever-changing variables (for example, if there are obstacles on the road, what lane markings look like, how fast a car can safely travel, etc.) While the following illustration is greatly simplified, it outlines the basic steps needed to build and deploy such a system and indicates which pillar each step belongs to.