Senior Data Scientist /Ai Engineer at Zantaz Data Resources
Real User
Top 10
Jul 31, 2025
My company doesn't have a partnership with Intel. It was a product that I needed and used. It is a company that creates a product which is developing, introducing AI features on this product. It's a data governance and eDiscovery tool. I was helping with this project to introduce AI capabilities on the product. The company itself, Santax AI, didn't have a partnership with Intel itself. From Intel's perspective, they are just users.
I recommend OpenVINO based on use case. It works well for one or two cameras but struggles when scaling to a large number. Overall, I rate OpenVINO around 6.5. My experience is from one and a half years ago, so current conditions might be different.
Systems and Solutions Architect at a tech services company with 1,001-5,000 employees
Real User
Mar 17, 2021
I'm not exactly sure which version of the solution we're using. I'm assuming it's fairly current as we're deploying our Edge IoT platform using it. While the solution is deployed on-premise, but we have the availability to hook into Amazon web services and Microsoft Azure. OpenVINO's part of Intel's framework and we're a gold partner with Intel. I would say it's a very good vision processing unit. It's a very good VPU if you're using Intel type of architectures as a co-processor. I have only really had experience with Intel with any OpenVINO based on the Movidius and therefore I'd like to get more hands-on time with others. However, generally, it's a good platform. It's worth exploring and can handle multiple camera streams, and it's straightforward to use. A company would benefit from trying it out. In general, I would rate the solution at an eight out of ten.
I would absolutely recommend OpenVINO. I think it's a good introduction to machine learning and inferencing. On a scale from one to ten, I would give OpenVINO a rating of nine.
Reading the documents is really helpful, and there are multiple people who use the software and share their quotes, ideas, and documentation. It really helps in understanding how to implement. If there are any questions, you can also use Intel's forums. The community is growing, which is really helpful for implementation issues. By using this solution, I have learned to not change the version too frequently. When I was implementing OpenVINO, a new version come out, and I updated the version, but the code was incompatible. So, I had to change things. One or two months later, a new version came, and I had to change all over again. I learned that I need some stability in the version. I would rate OpenVINO an eight out of ten for now. After two years or so, I would probably give it nine or ten. I believe they will change lots of things, and it will be one of the main products in the market.
Install the latest version that already has fixes for old problems. Work with some neural network, with a few layers to test it. If you use a neural network, like a fast R-CNN, it wouldn't work because it's too complex and there are some layers that are not recognized by OpenVINO. Start small and continue growing. Make an account in the Intel OpenVINO platform so you can start testing with Jupyter Notebook and send your inference jobs to different kinds of devices. Check the performance, know the differences between the different hardware, and how you can site the project. It is a good platform. The price I have seen is not so expensive. That's my advice. OpenVINO is a good tool if you want to work not in the cloud, you have to work on the edge. If you want to adapt your models, it has a good pipeline to follow. You have to learn it well because it could take time to debug some of the errors. Some of them are not very explained and you have to go through your codes a bit blind. I would rate it a nine out of ten because it's a very good tool. It's not complete but is the best in the market right now.
OpenVINO toolkit quickly deploys applications and solutions that emulate human vision. Based on Convolutional Neural Networks (CNNs), the toolkit extends computer vision (CV) workloads across Intel hardware, maximizing performance. The OpenVINO toolkit includes the Deep Learning Deployment Toolkit (DLDT).
The solution itself is excellent, and I would rate OpenVINO as a ten out of ten.
My company doesn't have a partnership with Intel. It was a product that I needed and used. It is a company that creates a product which is developing, introducing AI features on this product. It's a data governance and eDiscovery tool. I was helping with this project to introduce AI capabilities on the product. The company itself, Santax AI, didn't have a partnership with Intel itself. From Intel's perspective, they are just users.
I recommend OpenVINO based on use case. It works well for one or two cameras but struggles when scaling to a large number. Overall, I rate OpenVINO around 6.5. My experience is from one and a half years ago, so current conditions might be different.
I'm not exactly sure which version of the solution we're using. I'm assuming it's fairly current as we're deploying our Edge IoT platform using it. While the solution is deployed on-premise, but we have the availability to hook into Amazon web services and Microsoft Azure. OpenVINO's part of Intel's framework and we're a gold partner with Intel. I would say it's a very good vision processing unit. It's a very good VPU if you're using Intel type of architectures as a co-processor. I have only really had experience with Intel with any OpenVINO based on the Movidius and therefore I'd like to get more hands-on time with others. However, generally, it's a good platform. It's worth exploring and can handle multiple camera streams, and it's straightforward to use. A company would benefit from trying it out. In general, I would rate the solution at an eight out of ten.
I would absolutely recommend OpenVINO. I think it's a good introduction to machine learning and inferencing. On a scale from one to ten, I would give OpenVINO a rating of nine.
Reading the documents is really helpful, and there are multiple people who use the software and share their quotes, ideas, and documentation. It really helps in understanding how to implement. If there are any questions, you can also use Intel's forums. The community is growing, which is really helpful for implementation issues. By using this solution, I have learned to not change the version too frequently. When I was implementing OpenVINO, a new version come out, and I updated the version, but the code was incompatible. So, I had to change things. One or two months later, a new version came, and I had to change all over again. I learned that I need some stability in the version. I would rate OpenVINO an eight out of ten for now. After two years or so, I would probably give it nine or ten. I believe they will change lots of things, and it will be one of the main products in the market.
Install the latest version that already has fixes for old problems. Work with some neural network, with a few layers to test it. If you use a neural network, like a fast R-CNN, it wouldn't work because it's too complex and there are some layers that are not recognized by OpenVINO. Start small and continue growing. Make an account in the Intel OpenVINO platform so you can start testing with Jupyter Notebook and send your inference jobs to different kinds of devices. Check the performance, know the differences between the different hardware, and how you can site the project. It is a good platform. The price I have seen is not so expensive. That's my advice. OpenVINO is a good tool if you want to work not in the cloud, you have to work on the edge. If you want to adapt your models, it has a good pipeline to follow. You have to learn it well because it could take time to debug some of the errors. Some of them are not very explained and you have to go through your codes a bit blind. I would rate it a nine out of ten because it's a very good tool. It's not complete but is the best in the market right now.