Scalable Computer Vision Infrastructure
The heavy workloads and need for advanced computing make computer vision very difficult to scale. Running a deep learning model is fairly easy, and it only takes a few lines of code. However, the delivery and maintenance of complete computer vision systems require powerful software infrastructure. In computer vision, poorly optimized systems and outdated technologies cause enormous costs and poor performance.
Viso Suite is the world’s most scalable computer vision platform. The Viso solution enables organizations to build up AI capabilities internally, develop and maintain a portfolio of applications, and scale them to thousands of cameras in distributed systems.
Read our detailed guides about how Viso Suite enables scalability in Computer Vision:
- Horizontal Scalability: Scale to thousands of cameras and distributed servers
- Vertical Scalability: Build, deliver and maintain dozens of applications
- Organizational Scalability: Collaborate with large teams in secure workspaces
Point Solutions vs. Platforms in Computer Vision
Without a computer vision platform like Viso Suite, each new point solution requires its own hardware and software infrastructure that needs to be integrated and maintained. As a result, every additional point solution multiplies the complexity and maintenance load, leading to high costs and growing brittleness. Scalability is limited by separated infrastructures with little to no synergies.
Viso Suite provides one, shared infrastructure for all computer vision applications. Therefore, the synergies increase for each additional application, and the costs per application decrease at scale. The platform automates the integration and scaling of each application.
Infrastructure to Scale Computer Vision
To sustain high scalability, the Viso distributed architecture supports load balancing and removes single points of failure in the execution environment. Decentralized AI vision processing with machine learning on distributed edge devices allows the building of reliable, large-scale deep learning applications.
A simple computer vision application would then be able to grow to large cross-location deployment with thousands of endpoints connected without additional development. Just enroll more edge devices or endpoints into your workspace. Everything is fully automated, all required systems are automatically installed and securely configured.
Alternatively, you can deploy your applications to virtual devices to simulate multiple endpoints in your data center or edge servers.
