Enterprise Artificial Intelligence Transformation

Chapter 9Building the Perfect AI Engine

How hard is it to build an intelligent machine? I don't think it's so hard, but that's my opinion, and I've written two books on how I think one should do it. The basic idea I promote is that you mustn't look for a magic bullet. You mustn't look for one wonderful way to solve all problems. Instead you want to look for 20 or 30 ways to solve different kinds of problems. And to build some kind of higher administrative device that figures out what kind of problem you have and what method to use.

Marvin Minsky, professor of artificial intelligence at MIT

There is a long-held belief that the most important part of building an AI initiative is designing the AI application – the algorithm or software model created by AI scientists to make predictions. But as we have already seen, most of the effort that goes into building an enterprise AI application is spent on data gathering, cleansing, and labeling; creating data pipelines; DevOps; deployment; building business applications for end users; and monitoring and enhancing it over time. When companies ignore these stages, they often end up with highly inefficient modeling processes and people working in individual silos. This wastes money and time and delays getting business benefits from the model. The way to avoid this is to build a holistic AI platform.

AI Platforms versus AI Applications

An AI platform is a cohesive, well-integrated software solution running on scalable hardware that accelerates ...