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Edge AI · Intermediate

Model training & int8 quantization

Train a compact model on collected sensor data, validate its accuracy, and quantize it to int8 so it can run on constrained edge hardware.

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Project blueprint

What you'll build

This project turns a clean sensor dataset into a model that is small, measurable, and ready for deployment. You will train, validate, and quantize the model instead of treating the model file as a black box.

The result is a reproducible training workflow with accuracy checks that can be carried into the on-device inference project.

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Included build referenceBuild reference — dataset, training pipeline, and quantized model
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Project overview

Understand the build here. Learn and access the assets in the LMS.

This page covers what the project does, what you need, and how the build comes together. The LMS contains the complete lessons, source code, circuit diagrams, and deployment materials.

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Before you start

Build requirements

01Python development environment
02Collected sensor dataset
03TensorFlow and TensorFlow Lite
04Basic ML evaluation concepts
From setup to working system

Build sequence

Follow the steps in order, verify each checkpoint, and finish with a working project you can explain and extend.

01

Prepare the training dataset

Clean, label, split, and inspect sensor samples before training.

02

Train a baseline model

Build a compact model and establish an accuracy baseline.

03

Quantize to int8

Convert the model for edge deployment while measuring the accuracy trade-off.

04

Validate the artifact

Test the quantized model against held-out samples and package it for the next build.

Build the next checkpoint

Stop collecting tutorials. Ship a working build.

Use this project as a concrete piece of your embedded, IoT, or edge AI portfolio.

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