Yes. Viso Suite offers a fully integrated development environment to build, deploy and manage the entire application lifecycle. This facilitates the building of big and complex computer vision applications and high-scale deep learning systems.
With Viso Suite, you can create robust computer vision systems for mission-critical and real-time computer vision applications. Applications built with Viso scale from small applications to large enterprise computer vision systems. Distributed computing, governance, and security are part of this approach.
Scale Small Applications To Large AI Vision Systems
Most of our customers start with a prototype application to validate business value with internal stakeholders. The visual, model-driven development approach makes the computer vision workflows explainable and intuitive. This facilitates the collaboration between business and IT teams and saves a lot of time – for every iteration.
Once built, applications can be updated and maintained. The highly scalable infrastructure of the Viso Platform lets you distribute the applications to a large number of endpoints. Because Viso provides device management and remote monitoring and troubleshooting capabilities, you will not hit a wall as you move from piloting to operationalization, the most challenging part of computer vision adoption.
Build Custom Applications To Solve Complex Problems
From building hundreds of AI vision applications, some in niche industries with highly specialized problems, we have learned to build a solution that is flexible enough to solve any computer vision problem efficiently. The Viso architecture provides a set of specialized tools to overcome typical computer vision problems.
Our customers use Viso Suite to build not only popular computer vison applications but also highly specialized, complex image recognition systems. For example, industrial computer vision applications often come with strict requirements and involve the detection of custom objects.
Multi-Camera and Multi-AI Model Applications
Computer vision applications frequently require the combination of multiple computer vision tasks. A popular example is the combination of real-time object detection with object tracking to analyze object movement (for example, for counting). Another example would be face detection in combination with mask or face recognition. Often, deep learning methods are combined with image pre-processing and traditional computer vision.
The model-driven development approach of Viso Suite allows you to combine multiple AI models and processing methods seamlessly. You can swap AI models in existing applications or test different methods to compare performance. Viso manages the resource management that is needed to run complex applications on different hardware at the edge.
In the Viso Builder, you can also build multi-camera computer vision applications to process the video streams of several cameras in real-time. The ability to process multiple video feeds with one endpoint makes it possible to develop highly efficient systems.
