Enterprise AIOps

Chapter 1. Demystifying AI

A majority of analytics exist to take operational data (e.g., past/present stock prices) and provide focused insights (e.g., predicted stock prices) that inform decision making. This essential objective is the same for conventional business analytics and AI analytics and includes a range of functions (e.g., automation, augmentation, conversational AI for consumers, etc.).

The key difference is how developers create the code that transforms operational data into insights. For conventional business analytics, this is a static process where the developer manually defines each logical operation the computer must take.

AI analytics, via machine learning (ML), attempts to derive the necessary operations directly from the data, reducing the onus on the developer to create and update the model over time (but not eliminating the developer) and making it possible to address otherwise prohibitively sophisticated use cases (e.g., computer vision).¹

Machine Learning (ML)

A subset of AI, machine learning is the study of computer algorithms that improve automatically through experience and by use of data. ML algorithms build a model based on sample data, known as “training data” (defined in Chapter 5), to make predictions or decisions without being explicitly programmed to do so.

Beyond the initial challenge of the ML algorithm teaching an AI analytic to complete a basic task, we must ensure that it does not learn additional, undesirable behaviors that may impact ...