Building AI applications to run at the edge can seem like a formidable undertaking. But with the right development tools and platforms like the Intel® OpenVINO™ Toolkit 2022.1, it is easy to get started, streamline your effort, and deploy real-life solutions.
For a deep dive into the operational and business value of edge AI, I spoke to Adam Burns, Vice President of OpenVINO Developer Tools in Intel’s Network and Edge Group. Burns talks about the strategy in bringing new capabilities to OpenVINO 2022.1 and making it easier for developers to focus on building their applications. Our conversation covered everything from where to get started to solving the biggest AI developer challenges.
Let’s start by discussing what developers should know about building edge AI solutions.
At the end of the day, the edge is where operational data is generated. It’s in a store or restaurant where you’re trying to optimize the shopper or the diner experience. In medical imaging, it’s where an X-ray is taken. Or take a factory that wants to increase yields and manufacturing efficiencies.
Then you need to look at how AI marries up with an existing application. For example, in a factory, you’ve got a machine that’s running some part of the operation on the assembly line. You can use the data coming from that application to do visual inspection and ensure the quality of goods. Or you can use audio and data-based machine learning to monitor machine health and prevent failures. It’s this combination of how you use the data for the application and then use it to augment what the system is doing.
And the edge is very diverse. You have different sizes of machines, costs, and reliability expectations. So when we think about edge AI, we’re thinking about how we address a diversity of applications, form factors, and customer needs.
What was the strategy and thinking behind the OpenVINO™ 2022.1 release?
When we first launched OpenVINO, many of the applications for edge AI were focused on computer vision.
Since then, we’ve been working with and listening to hundreds of thousands of developers. There are three main things that we’ve incorporated into this release.
First and foremost is the focus on developer ease of use. There are millions of developers that use standard AI frameworks like PyTorch, TensorFlow, or PaddlePaddle, and we wanted to make it easier. For example, somebody is taking a standard model out of these frameworks and wants to convert it for use on a diverse set of platforms. We’ve streamlined and updated our API to be very similar to those frameworks and very familiar for developers.
When we think about #EdgeAI, we’re thinking about how we address a diversity of applications, form factors, and customer needs. @Inteliot via @insightdottech
Second, we have a broad set of models and applications at the edge. It could be audio, it could be natural language processing (NLP), or it could be computer vision. In OpenVINO 2022.1, there is a lot of emphasis on enabling these use cases, and really enhancing the performance across these diverse systems.
The third is automation. We want developers to be able to focus on building their application on whatever device or environment they choose. Rather than requiring a lot of parameters to really tweak and get best performance, OpenVINO 2022.1 auto-detects what kind of platform you’re on, what type of model you’re using, and determines the best setup for that system. This makes it very easy for developers to deploy across a wide range of systems without having to have optimization expertise.
