Enterprise Artificial Intelligence Transformation

Chapter 12Architecture and Technical Patterns

The new spring in AI is the most significant development in computing in my lifetime. Every month, there are stunning new applications and transformative new techniques. But such powerful tools also bring with them new questions and responsibilities.

Sergey Brin, co-founder of Alphabet

This chapter covers the technical architecture for the AI platform, expanding on the high-level description from Chapter 9 and going deeper into the subcomponents. To understand how the platform works, we will look in more detail at the four core layers we reviewed there as well as the elements within each layer. These layers are a data minder for data management, a model maker for model experimentation and validation, an inference activator for deployment and model serving, and a performance manager for ongoing production monitoring. These components support the AI lifecycle discussed in Chapter 8. We will also discuss design patterns for how to use the platform in various solution scenarios, including for chatbots and intelligent virtual assistants, personalization and recommendation engines, anomaly detection, physical IoT devices, and a digital workforce.

AI Platform Architecture

Rather than assembling an AI platform from the ground up, it is common to use commercially available, cloud-based, machine learning base platforms developed by reputable software companies. Microsoft Azure, Amazon AWS, and Google Cloud provide these base platforms, ...