История

Jim Bennett 0236acd7ce First cut of TinyML lab		2021-02-16 18:13:39 -08:00
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ai-edge	Fixing spelling	2021-02-16 18:13:26 -08:00
digital-agriculture/plant-monitor	Updating tracking codes	2020-11-17 11:09:45 -08:00
images	Adding data viz	2020-12-05 15:11:56 -08:00
iot	Tweaking azure iot lab	2020-12-15 14:00:03 -08:00
tiny-ml/audio-classifier	First cut of TinyML lab	2021-02-16 18:13:39 -08:00
README.md	First cut of TinyML lab	2021-02-16 18:13:39 -08:00
lab_contribution_guidelines.md	Updating tracking code to new format	2020-10-23 09:28:21 -07:00

README.md

Labs

This folder contains hands-on-labs, workshops and other content for using the hardware that comes with the IoT Cart.

These labs are divided up into multiple sections.

IoT

These labs cover traditional internet of things scenarios, connecting devices to the cloud

Environment Monitor - a beginners tutorial setting up a Raspberry Pi to send data to the cloud, and perform analytics on the data
GPS Lab - A sample application for sending GPS data from GPS sensor connected to a Raspberry Pi to Azure IoT Hub and displaying the location in a web application in real time using Azure Maps.
MXChip workshop - a hands-on lab for getting started building a cloud connected IoT device using the MXChip Iot DevKit prototyping board and Azure IoT Hub.
Hands on with IoT hub - a lab showing how to use Azure Services for building an IoT solution connecting simulated devices to an Azure IoT Hub instance and store that data in a storage account.
Smart door - a lab to build a smart door monitor prototype using an ESP32 microcontroller

AI/Edge

These labs cover running AI workloads either in the cloud from the IoT device, or on the edge running the workload actually on the IoT device itself.

OCR - Optical character recognition using a Raspberry Pi, USB camera and Python
Assembly line QA - A prototype of an AI image classification based quality assurance tool for manufacturing showing how to detect broken parts on an assembly line using AI, controlled from another device
Speech - Speech to text, text to speech and speech translation using a Raspberry Pi and USB microphone/speaker

TinyML

These labs cover training tiny machine learning workloads and running them on embedded hardware such as Adruino microcontrollers.

Audio classifier - a TinyML lab to capture audio data, use it to train a model, then classify audio data using that model on an Arduino Nano 33 Sense BLE board.

Digital Agriculture

These labs cover scenarios around digital agriculture.

Plant monitor - a plant monitoring lab based around a Raspberry Pi

Contributing

We love contributions! Please fork this repo and raise a pull request with new labs. Check out the lab contribution guidelinesfor guidelines on how to create a great lab that is consistent with the content we already have here.