At the Edge or in the Cloud?

Panel Discussion: Machine Vision 2025
On the second day of our virtual event inVISION Days experts from Amazon Web Services, EMVA, Intel and Xilinx joined Editor-in-Chief Dr.-Ing. Peter Ebert for a panel discussion about the future of machine vision.

Where do you see the main benefits of cloud based solutions for automation related vision tasks? What barriers are there to overcome to get customer acceptance for those solutions?

Myakov: Privacy laws and concerns are a big problem. When you transfer data from the edge to the cloud, it actually opens up your data for other risks as well, like theft or copying. But not all data is privacy tricky and yet we have to take the regulatories into account when designing our solutions.

Hall: There is a tendency within the industry to believe that edge computing or on premises computing is is more secure. Maybe because it doesn’t add the element of the transfer to the cloud. However, there is a bit of a fallacy here. In the context of devices which developers believe are to be used in a closed network, they decide not to take advantage of many of the security features that are available in modern devices. So I think the problem with security is not one of edge versus cloud as it is the overall thinking of the developer when they consider what it is exactly that they’re going to implement, whether it’s an edge device or a cloud connection.

Will Software-as-a-Service be sold more than vision systems and components in the future? And who will offer the new vision systems?

Metzner: The question in the end is who will build the solution and who is offering and who is integrating the solution? A solution consists of many different components, so it’s a broad ecosystem in which we all need to work together.

Myakov: Different companies have different specialties and I don’t think that we are gonna see one to rule them all in the near future. There are some niche examples when such power is concentrated in one hand and but there are very few of those. But typically ecosystems are multilayered: you evolve and you focus on something and then you excel in it.

Diani: What I saw over the last 10 years is a big revolution. The machine vision market is going up to very high speed and high resolution applications but also to the bottom where the application doesn’t need a lot of power. So I believe that there is space for everyone.

Quenton Hall
Quenton HallBild: TeDo Verlag GmbH

Hall: The reality is that the problems that we have today are best solved through a marriage of these different technologies. For example, if we want to take novel data from any camera from any geographic region, push it back up to the cloud, and to develop in an iterative way a more robust machine learning model which can then be redeployed to all those devices without anybody plugging a USB stick into the camera, how do we get there? How do we work with OEM’s? How do we work with system integrators? How do we work with end customers to provide solutions like this?

Myakov: The democratization of deployment is a missing piece right now. You can train a model but putting it on a camera is complex because tho thirds of the market globally is run with a very closed ecosystem approach. So essentially there are cameras, but there’s no way for anybody to deploy on them. In machine vision the situation is different, but it’s a niche market. But if you think globally, that’s what’s gonna actually impede the progress. And I totally agree that training in the cloud and deploying anywhere you want is what’s going to drive innovation. I don’t think we have uncovered all of the use cases out there just yet. So having the ability to deploy on any device would be important and instrumental in driving that innovation, but there are some very objective barriers to that.

What is your best estimate as to when it will be completely the norm to store and share quality and production data via the cloud?

Metzner: This is already the norm. Therefore, if you haven’t done it yet, you need to prepare for it. There are barriers that you need to overcome. Some technical challenges, let’s say on the networking side. But it can be done. The real problem is in the mind. If you are afraid of new things, you will not change it. This is actually the barrier that we are facing most of the time.

Myakov: Things which require low latency, they will stay at the edge and that’s not going to change. Only things which are practical for the cloud will go to the cloud. And I agree that security in the cloud done by professionals is better than security at the edge done by non-professionals. But again, there will be regular regulatory barriers and whether real or perceived. So it’s gonna be a healthy mix of edge and cloud. I think probably by 2025-2027 the ratio between cloud and the edge is going to be clear.

Marco Diani
Marco DianiBild: TeDo Verlag GmbH

Diani: You need to have your mind open to new technology. The cloud is coming and a lot of things are changing, but if you don’t have an open mind you will not accept these changes. But we need people that believe in the technology. The market is growing. The technology is growing. I know that machine vision is a niche market but is a market that is a very interesting, completely different from many other markets. The only thing that I don’t see now in cloud computing is some standardization for the acquisition of images. I believe that big companies can drive this standardization.

Hall: I think it is clear from this discussion that there is this unique opportunity to collect and correlate data and make that data available in the cloud. So we have this interesting new challenge ahead of us. How do we marry the capabilities of cloud in terms of data collection, data annotation, model training and connect that with Edge deployment? I think all of the pieces are there and in the next three to five years we will achieve a state that is more clearly defined.

Teilnehmer

Jan Metzner, Specialist Solutions Architect Manufacturing, AWS

Marco Diani, CEO of Image S, EMVA

Alexey Myakov, Chief Computer Vision Advocate, Intel

Quenton Hall, System Architect, AMD/Xilinx

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