Picking an Edge AI Application Development Platform doesn’t seem that complicated. That being said, choosing the wrong platform could impact your productivity and the transition from development and training to inference and production.
As the evolution of Edge AI continues to shift towards executing analytics at the data source, it is important to point out how the environment on the edge impacts platform technology choices. Let’s take a closer look at some of the considerations:
- Build the app on the same platform you plan to deploy the solution on. Performance and model behavior become increasingly important as you roll out your applications. The training platform should be similar to the production platform so you can appropriately judge how the model will perform when moved into production.
- 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.
- Edge environments need low power but lots of capability. This is 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 has to reflect these realities of analytics at the edge.
- Decide on 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 use. 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 OPEX budget.
- 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. Generic computing platforms for Edge AI may work, but it is not an optimized solution and can negatively impact your time to solution and ROI. In today’s competitive environment you need every advantage, so it’s worth some time to explore your options.
While you’re exploring your options, why not take a deep look into what Cachengo can do? Cachengo is an Edge AI platform that can support your Edge AI project with purpose-built hardware and an operating environment that makes model development and deployment easy. Our marketplace allows you to pick from pre-existing modules or you can add your own apps for your specific industry.