It is an exciting time in AI (Artificial Intelligence). ChatGPT is revolutionizing the world, and one of the best-known secrets of Microsoft’s platform is set to revolutionize the world of vision AI processing, through a deep technical collaboration with NVIDIA & Microsoft.

Machine vision cameras

Today, machine vision cameras are commonly deployed worldwide for visual inspections. These cameras enable people to see what they cannot see with the naked eye and have many use cases. They are frequently used in manufacturing settings for quality control on production lines and in detail-required industrial scenarios. Basler & Allied vision are leading producers of these cameras, which operate against the Gig-E vision protocol. NVIDIA announced compatibility and substantial investment in this protocol at GTC in March. Today, these cameras can be used to identify quality control issues and stitched together into any solution supporting and running with DeepStream.

The importance of hardware acceleration

However, these cameras come with extremely specific requirements. Hardware that can process multiple streams of high-fidelity video coming from high frame rate cameras must be able to provide the right form factor, operating environment constraints, and processing requirements. Fortunately, NVIDIA’s GPUs (graphics processing units) can be leveraged for parallel processing at scale. This idea of massively parallelized hardware acceleration is in testing today for deployment on Azure Stack Edge and other Azure Certified Devices and will enable at-scale GPU deployment for hardware-accelerated machine vision running at the edge and connected to the cloud.

Edge and cloud

Finally, Azure’s Cognitive Services team’s unique and innovative Florence models (in preview today) enable customers to rapidly create and innovate upon cloud models – to accelerate edge-to-cloud hybrid AI processing workloads development. Azure’s Spatial Analysis cognitive service already supports edge deployments of certain industrial use cases on Azure Stack Edge. Leveraging these tools also allows for both edge deployments very rapidly and for hybrid deployments – leveraging custom models at the edge to identify and pass relevant frames to the cloud for deeper analysis.

Introducing Vision as a Service

Fractal has created Vison Analytics as a Service, an offering based on the repeated implementations of cloud-connected edge intelligence deployed by their Edge & IOT team. This stitches together the innovations offered by Microsoft and NVIDIA through a Managed Application, available to customers for a simple price. This simple managed offering allows for unified and integrated pricing as per the scale factors & standard requirements of a complex vision implementation, with an initial implementation of vision AI deployed in a 3-month timeline. It leverages standard templates and accelerators from Microsoft and NVIDIA, like the Azure DeepStream Accelerator, the NVIDIA GPU sharing work, MEC app solution accelerator, and the innovations mentioned above to enable a rapid deployment package for customers wanting to implement video AI solutions at scale.

This offering also provides several optional industrial extensions, including camera management, coordination with sophisticated AI engines, and integration with critical industrial equipment assets. One common use case is the deployment of deep reinforcement learning to fully automate command and control by leveraging a physics-based simulation on a manufacturing line, to drive activity after visual inspection. It can also be integrated with PLC control systems and other required industrial assets, as a direct extension, making it an incredibly useful solution for organizations wishing to upgrade their quality control processes with vision AI.

The goal with this offering is to enable as many customers as possible to unlock the value of vision AI by leveraging Fractals capabilities with the greatest ease possible.

We also offer a free consultation to scope a vision AI engagement and identify a hardware recommendation, edge & cloud strategy, audit camera requirements, scope the required skills, and any required DRL (Deep Reinforcement Learning) deployments. This standardized pricing is designed to offer greater scale, while Fractal leverages vision intellectual property development alongside Microsoft and NVIDIA’s offerings, enabled by this offer.