The Fundamental Problem of Scaling Computer Vision

Security Cameras

"To a man with a hammer, every problem looks like a nail."

The reason Computer Vision type applications and AI in general is so hard to scale is simple: The type of skills used to build the core of the solution are not the same as those required to maintain, release, deploy, and scale. In fact they are very different, even at very basic levels.

Let's try and break a simple example it down into a few basic components to illustrate the problem. Let's assume we want to identify moving forklifts in a warehouse with the purpose of deducting their idle vs. active time, using already installed security cameras. The business case is a potential 10-20% reduction in lease costs associated with the equipment.

The elements in such a solution we have to consider are [vastly simplified]:

  1. Creating an AI module to identify forklifts
  2. Create a system for tracking forklifts and outputting statistics data
  3. Managing up to 30 video streams from existing security cameras across thewarehouse
  4. Utilising GPU
  5. Make it maintainable using a containerization solution like Docker
  6. Ensure that the system works across 50 different warehouses with different forklifts and lighting conditions
  7. Validate that every single AI- and code change still works across the 50 deployed warehouses prior to deployment

And here is the problem. Typically the people doing 1 will do so using Python, PyTorch, Tensorflow or similar to create Neural Networks. However, 2, 3, 4, and 5 require different enterprise tools and skillsets - typically .NET, unit testing, Docker, and Enterprise Architecture. 6 and 7 require DevOps people with a good mix of project management, release management, test architecture, etc.

The vast majority of implementation projects we see gradually realise this, and thus have a rapidly escalating cost of development and maintenance, reducing the viability of the business case.

This is why we built the Sentispec Core AI platform.

Simply put, the platform aims to solve the scalability problem in its entirety, such that as a customer you can focus on building the value adding new AI models on top, and the rest is provided for.

The platform ensures multicamera support, an easy and fast way of building new AI pipelines, GPU support, a comprehensive continuous validation framework, a statistics portal, and much much more.

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