5 tips for Picking an Edge AI Platform

This post was originally published by Matt Mayo Editor at KD Nuggets

Edge Analytics isn’t just coding and tools. The different environment outside the datacenter or cloud means a purpose built platform is the best way to deliver consistent results. We discuss 5 different considerations for an edge platform to support your training and deployment.

Picking an Edge AI Application Development Platform doesn’t seem that complicated. Picking the wrong platform could impact your productivity and the transition from development and training to inference and production. As momentum continues to move to executing analytics at the data source, it is important to point out that the environment on the edge impacts platform technology choices. Let’s take a look at some of the considerations.

  1. Decide your data strategy- where does the data come from – that’s where you want your Edge AI. Some data you might want to keep forever. Some data will be of transitory value. If you are subject to audits to justify your training models you’ll want to keep the data that was used in training available. Having a well thought out data strategy can help you avoid costly mistakes and wasted money.

    5 Tips for Picking an Edge AI Platform

  2. Build the app on the same platform you’ll deploy the solution on. Performance and model behavior are important as you roll out your solution. The training platform should be similar to the production platform so you can appropriately judge how the model will work when moved into production.
  3. Build the app on a platform that supports the entire application stack (e.g. custom databases, web servers, and caching). You need to support training and inference but don’t forget the other aspects of your application stack that also need support. Having a highly capable node designed for the edge is important for flexibility and ROI.
  4. Edge environments need low power but lots of capability. This could be one of the biggest reasons that you want the development and production platform to be the same. The edge is a different world from the data center in terms of available power and space. Your platform choice has to reflect these realities of analytics at the edge.
  5. Security is important, how do you get a secure network to protect your IP? Traditional approaches to security like a VPN can still leave you exposed to a man in the middle attack or spoofing. A zero trust network that authenticates end points is more secure and should be considered.

Picking an Edge AI platform does require balancing a number of factors, and hopefully this look into some of the considerations is helpful. Generic computing platforms for Edge AI may work, but it is not an optimized solution and can impact your time to solution and ROI. Commodity off the shelf (COTS) hardware makes sense in some environments but perhaps not at the edge. You want an edge system for your edge analytics. In today’s competitive environment you need every advantage, so it’s worth some time to explore your options.

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This post was originally published by Matt Mayo Editor at KD Nuggets

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