Microsoft Certified: Azure AI Engineer AssociateImplement image and video processing solutionsMedium
A security firm is developing an automated surveillance system for a remote industrial site. The system needs to detect trespassing events (e.g., unauthorized person entering a restricted area) in real-time. Due to limited internet connectivity at the site, processing must occur locally with minimal latency. Which Azure AI deployment option for a custom vision model should be chosen?
- ACloud endpoint deployment
- BIoT Edge deployment
- CAzure Functions with Computer Vision SDK
- DAzure Batch AI
Show answer & explanationAnswer & explanation
Correct answer: B. IoT Edge deployment
IoT Edge deployment allows custom vision models to run directly on local devices (edge devices) at the site, enabling real-time processing with low latency and reducing reliance on continuous cloud connectivity, which is critical for remote sites with limited internet.
Why the other options are wrong
- A. Cloud endpoint deployment requires constant, reliable internet connectivity for every inference.
- C. Azure Functions still execute in the cloud, requiring internet access for processing.
- D. Azure Batch AI is for large-scale training jobs, not real-time inference at the edge.
Custom Vision IoT Edge Deployment
Deploying a custom-trained Azure Custom Vision model onto an IoT Edge device for local, low-latency inference.
- Enables AI processing at the edge, close to data sources
- Reduces latency and bandwidth usage
- Allows for offline operation with intermittent connectivity
- Requires an IoT Edge runtime environment
Memory trick: IoT Edge brings the AI 'brain' right to the device, not just the cloud.