close

CVOps

CVOps

CVOps is a category that describes the enterprise business process, job role, and enabling tools for delivering Computer Vision in production.

What is CVOps?

Computer Vision works best when it is focused on a specific task and optimized for a specific
context. Cameras that look out your front door (at your porch and driveway) are doing a different
job to the camera looking down on your backyard from the roofline. Both watch for people to keep
you safe, but in the world of AI these are very different tasks.

CVOps is the enterprise software process for delivering the right Computer Vision to the right
camera at the right time. A reliable, accurate CV system needs to collect data over time (to deal
with factors like “Data Drift” and “Model Drift”), and to deliver software updates that adapt to the
changing realities of our physical world.

CVOps is best enabled by specialty software that securely and reliably manages the “learning loop”
of data collection, AI updates, deployment, and monitoring.

CVOps made Orchestrait possible

CVOps is a new category and Orchestrait (the AI management platform within this category) is a new way of doing things.

Orchestrait gives you the ability to create and manage your own IP instead of getting someone to do it for you. This is a groundbreaking option that, until now, hasn’t even existed.

With Orchestrait, you can create custom AI and take control of your own AI destiny

Where did CVOps come from?

Early solutions in AI based Computer Vision required massive compute infrastructure and had to be executed in the cloud. These systems required exponentially more data and computing power to accurately address the diversity of the real world.

Technology trends of Edge Deployment and Ensemble AI emerged to address the problems of AI bloat – but created a new challenge in the logistics of matching data and AI to the cameras and use cases.

CVOps is an enterprise function incorporating the best practices of data collection, AI updates, release management, and performance monitoring to ensure high quality service and excellent customer experience.

What this mean for your Computer Vision AI strategy?

1 Scaling CV cost-effectively requires a CVOps strategy
2 Maintaining customer satisfaction over time requires CVOps strategy (due to Drift, and evolving customer expectations).
3 CVOps tools focus on the unique challenges of vision-based systems, adding more value than generic DevOps or MLOps systems.
4 Release management to Edge devices should incorporate Quality Assurance testing and phased rollouts. A CVOps system should provide integrated process management with best practices to help ensure enterprise service levels.
5 Building all of this in house will take a team of engineers 76 full-time months (USD$1.6m) on average. It’s a lot harder than you think!

Frequently asked questions

CVOps describes the enterprise business process, job role, and enabling tools for delivering Computer Vision in production. 

A reliable, accurate CV system needs to collect data to deal with factors like “Data Drift” and “Model Drift,” and to deliver software updates that adapt to the changing physical world. CVOps delivers the correct Computer Vision to the correct camera at the correct time.

Past solutions in AI based Computer Vision needed immense compute infrastructure and had to execute in the cloud. These systems required exponentially more data and computing power to accurately address the diversity of the real world. 


Technology trends of Edge Deployment and Ensemble AI were created to address the issue of AI bloat - but they also created new challenges in the logistics of matching data and AI to the cameras and use cases.

  1. Scaling CV cost-effectively requires a CVOps strategy. 
  2. Maintaining customer satisfaction over time requires a CVOps strategy (due to Drift, and evolving customer expectations). 
  3. CVOps tools focus on the unique challenges of vision-based systems, adding more value than generic DevOps or MLOps systems. 
  4. Release management to Edge devices should incorporate Quality Assurance testing and phased rollouts. A CVOps system should provide integrated process management with best practices to help ensure enterprise service levels.
  5. Building all of this in house will take a team of engineers 76 full-time months (USD$1.6m) on average. It’s a lot harder than you think!

CVOps is best enabled by specialty software that securely and reliably manages the “learning loop” of data collection, AI updates, deployment, and monitoring.

Trusted by

Xailient’s commercial partners

Press releases

March 14, 2022

Xailient specializes in extremely efficient low-power computer vision. Intel's OpenVINO specializes in maximizing the performance and speed of computer vision AI workloads. OpenVINO improved Xailient FPS 9.5x on Intel hardware to 448 FPS. Together, Xailient-Intel outperforms the comparable MobileNet_SSD by 80x. Even after Intel worked the OpenVINO magic on MobileNet_SSD, Xailient-OpenVINO is 14x faster.

November 29, 2021

Xailient’s Face Recognition enables high-speed edge AI processing with low-power consumption using Sony’s IMX500 – a chip so small it can fit on the tip of your finger.

Explore our blogs

We see things differently in the dynamic field of computer vision AI

Get started with Xailient

We empower companies to bring computer vision AI products to market
faster and with less investment

OnEdge Newsletter

A weekly newsletter with the best hand-picked resources about Edge AI and Computer Vision

OnEdge is a free weekly newsletter that keeps you ahead of the curve on low-powered Edge devices and computer vision AI.

 

You’ll get insights and resources into:

  • Edge computing use cases.
  • Market trends in Edge computing.
  • Computer Vision AI at the Edge.
  • Machine learning at the Edge.
Cookie Policy