Microsoft doesn’t have an official certification for an AI Edge Engineer yet, but I have a feeling one is on the horizon. The term Edge continues to insert itself into a number of products. There’s even a version of Azure Stack Edge developed specifically for Edge compute devices. If this whole Edge concept is new to you, don’t worry, you’ll hear a lot more about it in the coming years.
IoT Alone isn’t Enough
IoT isn’t new. It’s been with us for 20 years. Longer if you count the many systems developed for the manufacturing and agriculture industries. The idea is to gather telemetry data, things like machine vibrations, humidity levels, or light levels, and send that data to a central data store for processing. The processing tends to involve searching to actionable insights. Can I build a correlation between machine vibration levels and machine breakdowns. If I can, I can add predictive maintenance triggered on vibration. Same goes for watering plants in a field or adjusting light levels in an office building.
IoT is still valid for these operations, however the demand is increasing for devices and intelligent actions that happen closer to the source. Why send constant streams of telemetry data to the cloud when you know the parameters that trigger an event? Why not bring the event trigger closer to the device? And what if you need greater levels of intelligence, like vehicle recognition in a shipping yard or warning zones on drilling platforms that recognize when a human not wearing the appropriate safety attire has crossed into that zone? For these types of situations you need something more sophisticated than the basic telemetry gathering tools. You need devices with the sort of AI typically hosted in the cloud brought to the edge of the cloud. Edge devices. More powerful systems that can serve as telemetry gathering systems, decision systems, and routing gateways. This is what Edge Computing is all about.
Developing for The Edge
Microsoft has developed a suite of tools that make working with edge technologies fairly simple. It’s not outside the realm of possibility that in the next few years Edge developers and Edge Engineers will be in high demand. The technologies used today are IoT Hub, IoT Edge Development kits, Edge enabled devices (which can be as simple as a PC), containers like Docker. Cognitive services like Computer Vision can be loaded onto these devices in the form of modules that are managed by Edge Agents and routed to the cloud or other devices using Edge Hubs and routing. These developers will need an understanding of developing for IoT devices, as well as development for the cloud. Azure provides many services for making this possible. There are services like IoT Hub and the Device Provisioning Service that make managing devices and integration with backend services possible. There’s also a SaaS platform called IoT Central that covers the majority of the use cases for specific industries that want to begin their journey into IoT Edge data processing.
These are the platforms that enable AI at the Edge using Microsoft Azure technologies, but these certainly aren’t the only tools that are taking advantage of this particular pattern. AWS, Google, NVIDIA, and many other manufacturers and services have staked a claim in this burgeoning field.
A New Type Of DevOps
DevOps is the word that has come to replace agile as the term to best describe the marriage of development and operations in the service of quickly delivering working systems to market. MLOPs is a similar term used to describe the process of bringing machine learning and AI to market. I’m not aware of a term specific for Edge development, perhaps EdgeOps should be the label, but what’s important to understand is that for this type of development there is a transformative workflow.
For a greenfield engagement, you probably won’t start with AI. You’ll probably start with telemetry in order to gain insights and develop actionable plans based on the data. This means you need a deployment pipeline that allows a device to go from being a simple hub or gateway for telemetry data to a more sophisticated application platform. Intelligence from the cloud moves from cloud services to Edge containers that are utilized by the Edge devices. This requires an evolutionary Edge network that can grow more responsive and cost effective as the business evolves its goals and strategies around gathered data.
This is an enormous opportunity developers and engineers, as well as value added providers, delivery partners, and definitely the organizations who embrace this new way to capture and manage data and events.
Why Did I Follow this Path?
For most of my career I’ve worked with different organizations that have struggled with their data. There are a large number of anti-pattens deployed by companies that make their data nothing more than a trailing indicator asset. It’s rarely a form of strategic information and even more rarely an active asset helping to shorten the loop between value delivery and cash flow. I decided not too long ago that I wanted the focus of my career to be toward that goal; helping organizations use their systems and data to shorten that loop. This isn’t always a technology solution. Sometimes achieving this goal requires organization change. I’m regularly looking for people, tools, and processes that I can utilize to help deliver on that goal.
Becoming an AI Edge Engineer as well as diving deeper into the world of Data Science seem the natural extension of that goal. If you are interested in exploring this path, you should take a look at Microsoft’s related training materials as well as the Oxford course mentioned on the training page: https://docs.microsoft.com/en-us/learn/paths/ai-edge-engineer/
Additionally, I’ll continue to post my lab work related to IoT Edge development and my thoughts on how this applies to helping businesses use their data to achieve their financial goals on this blog.