Is the era of Enterprise Architecture dead? Today, we model our IT organizations much like the agile systems we are developing — loosely coupled, independent, and self-governing. Driving decisions down to the experts and delaying those decisions as long as possible is achieving better agility and alignment with business objectives and end-user experiences. The traditional hierarchal, top-down driven and waterfall design and decisioning is no longer adequate in the age of Agile. Where does this leave the Enterprise Architect? Dead if they do not adapt. However, as the Agile pendulum is swinging back to moderation, the community is re-emphasizing overlooked…


Overview

This article looks at the unique challenges introduced by Edge computing for AI/ML workloads, which can have a negative impact on results. It applies available machine learning models to real-world Edge datasets, to show how these challenges can be overcome, while preserving accuracy in the dynamic nature of Edge environments.

Context

The field of machine learning has experienced an explosion of innovation over the past 10 years. Although its roots date back more than 70 years when Alan Turing devised the Turing Test, it has not matured significantly until recently. Two primary contributing factors are the exponential growth in both compute…

Brent Taylor

A digital innovation advisor to some of the largest clients in the world with over 25 years’ experience realizing business outcomes with technology.

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