Deploying machine learning models at the edge with minimal latency. Practical guides for Ottawa startups building intelligent applications.
Whether you're running inference on IoT devices, mobile phones, or local servers, we've covered the fundamentals, tools, and real-world patterns that work. No cloud dependency required.
Moving AI inference to the edge eliminates cloud latency, reduces bandwidth costs, and keeps sensitive data local. For startups building real-time applications — from autonomous vehicles to industrial monitoring — edge deployment isn't optional. It's the foundation.
Practical resources on model optimization, hardware selection, and deployment strategies for edge AI.
Learn how to reduce model size by 80% without losing accuracy. Covers post-training and quantization-aware training approaches.
A practical comparison of NVIDIA Jetson, ARM processors, and Intel options. Includes cost breakdowns and real-world performance benchmarks.
Which framework should you choose? We break down deployment complexity, model compatibility, and performance across different edge devices.
End-to-end walkthrough of a production edge system. Covers data collection, model serving, and handling edge failures gracefully.
