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The AI Edge Talent Stack

Building a Talent Stack

Last week I started my studies with the Oxford continuing education course, Artificial Intelligence: Cloud and Edge Implementations (online). For me to attend this class, I have to wake up at 3 am on Saturday morning, but so far that little sacrafice has been well worth it. What I’m hoping to get out of this course are a few key skills. First, I want a better understanding of data science taught from people who know it, practice it, and apply it to real world scenarios. Recognizing cats and hot dogs is a good start, but I want AI that helps people to derive greater value from their existing information systems. Additionally, I want to take my IoT skills to the next level. And of course these things come together to build the AI Edge MLOPs process.

I was recently introduced to the concept of a Talent Stack from Linda Zhang’s blog and it’s something I’ve been unknowingly building over the last year. I’m seeing that stack start to look like the following:

  • IoT device programming
  • IoT networking
  • IoT cloud architecture
  • Lambda data architectures
  • Data analysis
  • Machine Learning
  • Orchestrated container management (k8s)
  • Python, Scala, and Apache Spark
  • MLOPs
  • Reactive Engineering
  • Domain Driven Design (Event Storming)
  • Systems Thinking
  • Wardely Mapping

The Oxford course touches many of those areas where I want to build skills.

What does this stack do?

A diagram depicting the relationship of cloud computing to fog computing to digital twins

It’s clear that technology is pushing us away from direct interaction with a single machine, like a mobile device or a laptop, and closer toward an environment of conntected devices. I don’t belive that traditional user interfaces will necessarily go away, but their role in capturing data will be deminished when AI on devices allow us to better communicate with smart objects around us. We will likely always use some type of mobile device, and we will likely always have some type of personal computer powerful enough to perform more demanding tasks, but when we can we will interact with smart devices.

I believe these smart devices will be found in the places we work, where we shop, and where we exercise our leisure. Businesses, cities, and the entertainment industry will need people who are skilled in building safe, secure, unbiased (but perhaps opinionated,) AI and integrating these AI with cloud computing, fog computing, and using digital twins for real objects to interact with reality.

To help add value to this new world I’m learning all the skills necessary to help teams and businesses achieve these goals. This is an area where I see a lot of potential for growth.

As I grow on this journey I want to help other developers who are interested in taking on these new challenges. I’m focusing some of my future blog posts on this subject so that we can start building a community around the concepts related to AI Edge Engineering. As that matures, I’ll share more of what that will look like. Until then, keep reading to follow along on my adventure.

Photo by Matt Hardy on

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