Running AI inference at the edge means no network latency, no API costs, and no data leaving the device. This course covers quantization, TFLite, ONNX, and the monitoring stack for models running on embedded hardware.
This is a text-first course that links out to the best supporting material on the internet instead of trying to replace it. The goal is to make this the best course on edge ai and model deployment you can find — even without producing a single minute of custom video.
This course is built by people who ship production edge systems for a living. It reflects how things actually work on real projects — not how the documentation describes them.
Every day has working code snippets you can paste into your editor and run right now. The emphasis is on understanding what each line does, not memorizing syntax.
Instead of shooting videos that go stale in six months, Precision AI Academy links to the definitive open-source implementations, official documentation, and the best conference talks on the topic.
Each day is designed to finish in about an hour of focused reading plus hands-on work. You can do the whole course over a week of lunch breaks. No calendar commitment, no live classes, no quizzes.
Each day stands alone. Read them in order for the full picture, or jump straight to the day that answers the question you have today.
When to run inference at the edge vs in the cloud. Latency, cost, bandwidth, privacy, and the hardware constraints that make model optimization non-optional.
Quantization (INT8, FP16), pruning, knowledge distillation, and layer fusion. How to get a 100MB model down to 5MB without losing accuracy.
Converting TensorFlow models to TFLite, the interpreter API, hardware delegation (GPU, DSP, NPU), and deploying to Android, iOS, and Raspberry Pi.
The ONNX format as a universal model exchange layer. Exporting from PyTorch, converting to ONNX Runtime, and targeting different hardware backends.
Shadow deployments, A/B testing for edge models, OTA model updates, and the telemetry patterns that tell you when an edge model is degrading.
Instead of shooting our own videos, Precision AI Academy links to the best deep-dives already on YouTube. Watch them alongside the course. All external, all free, all from builders who ship this stuff.
Full deployment walkthrough of a TFLite model on Raspberry Pi — from model conversion to live inference.
INT8 vs FP16 vs FP32, post-training quantization vs quantization-aware training, and the accuracy-performance tradeoff.
Converting PyTorch and TensorFlow models to ONNX and running inference with ONNX Runtime on CPU and GPU.
System design for edge inference: model serving, OTA updates, and monitoring at the edge without cloud connectivity.
The best way to understand any technology is to read the production-grade implementations that prove it works. These repositories implement patterns from every day of this course.
The TFLite converter and runtime are in tensorflow/lite. Reading the delegate interface explains how hardware acceleration is plugged in.
Microsoft’s cross-platform ML inference engine. The execution provider interface shows how it targets CPU, CUDA, CoreML, and NPU.
The open standard for ML model exchange. Reading the operator spec clarifies what gets preserved and what gets lost during model conversion.
The source of most models you will convert to ONNX. The torch.onnx export module source explains exactly what dynamic axes do.
You work with microcontrollers and embedded Linux. This course bridges your hardware expertise to the model deployment and optimization layer.
You train models. This course teaches the optimization and conversion pipeline that makes them run on constrained hardware.
You build IoT products. On-device inference eliminates the cloud dependency. This course gives you the path from cloud model to edge deployment.
The 2-day in-person Precision AI Academy bootcamp covers edge AI and model deployment hands-on. 5 U.S. cities. $1,490. 40 seats max. June–October 2026 (Thu–Fri).
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