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

  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!

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.

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.

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