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EDWartens UK

Free course · AI and machine learning

Edge AI and TinyML for Industrial Sensing

Put machine learning on the sensor: vibration, current and sound from motors, fans and pumps, sampled correctly, turned into spectral features, classified or scored for anomalies by a tiny model on an ESP32 or Arduino, quantised to fit, sent over MQTT or Modbus to a Node-RED dashboard, and deployed with a battery budget, updates and a drift check. Vision on a Raspberry Pi AI HAT and Jetson too. Hardware optional.

See the modules

Free to learn. Verifiable certificate £19 when you pass the final exam (optional, VAT included).

Level
Intermediate
Modules
13
Video
18 hours
Price
Free

Outcome

What you will be able to do

Decide which plant AI belongs at the edge; choose a board from memory, compute and power figures; sample vibration, current and sound at the right rate; compute windows, FFTs and spectral features; train, evaluate and convert a tiny network for LiteRT for Microcontrollers; run Edge Impulse from data to deployment; build an anomaly detector with a threshold and debounce on an ESP32; classify machine sounds; quantise to int8 and build a memory and latency budget; run YOLO on a Raspberry Pi AI HAT or Jetson; publish results over MQTT and Modbus to Node-RED; and deploy with a battery budget, over-the-air updates and a drift check.

  • Decide which plant AI belongs on the sensor node and which in the cloud, with the data rates worked out
  • Choose between ESP32, Arduino Nano 33 BLE Sense, STM32, Raspberry Pi with an AI HAT and Jetson from memory, compute and power
  • Set a sample rate from shaft and bearing frequencies, and read vibration, motor current and sound safely
  • Turn signals into windows, FFTs and spectral features, and train a tiny network for LiteRT for Microcontrollers
  • Build an anomaly detector on an ESP32 that learns normal, with a threshold and debounce set from data
  • Classify machine sounds, quantise models to int8 and write a memory and latency budget
  • Run YOLO on a Raspberry Pi AI HAT or Jetson, and publish results over MQTT and Modbus to Node-RED
  • Deploy in the field with a battery budget, over-the-air updates with rollback and a monthly drift check

Software

Edge Impulse (free Developer plan, in the browser), Arduino IDE 2, Google Colab with TensorFlow and LiteRT, Ultralytics YOLO, Mosquitto, Node-RED and the Wokwi simulator. All have a free route.

Hardware

Optional. Every module can be done with a smartphone as the sensor, the Wokwi simulator and public datasets. To build the real node: an ESP32 or Arduino Nano 33 BLE Sense with an ADXL345 or MPU6050 accelerometer and a small fan; a Raspberry Pi 5 with an AI HAT or a Jetson Orin Nano is optional for the vision module.

Syllabus

13 modules

Each module has video lessons, notes and a short quiz. The final exam closes the course.

  1. 1Edge or cloud for plant AI: latency, bandwidth, reliability and cost3 lessons
  2. 2The hardware map: microcontrollers, Raspberry Pi with an AI HAT, and Jetson3 lessons
  3. 3Collecting sensor data: accelerometer, current clamp and microphone4 lessons
  4. 4Features for signals: windows, FFT and spectral features3 lessons
  5. 5Training a tiny neural network for LiteRT for Microcontrollers4 lessonsLab
  6. 6Edge Impulse end to end: data, impulse, test and deploy4 lessons
  7. 7Anomaly detection on a motor or fan with an ESP323 lessonsLab
  8. 8Sound classification for machine states4 lessons
  9. 9Quantisation, memory and latency budgets3 lessons
  10. 10Vision at the edge: YOLO on the Raspberry Pi AI HAT, Jetson and ESP32-CAM3 lessons
  11. 11Getting results out: MQTT, Modbus and Node-RED dashboards3 lessons
  12. 12Field deployment: power, enclosures, updates and drift4 lessons
  13. 13Final assessment1 lesson

How it works

How this course works

  1. Watch the video lessonsOne module at a time, at your own pace.
  2. Take the module quizA short quiz at the end of each module shows what stuck.
  3. Keep your notesWrite in My notes beside each module and ask the trainer if you are stuck.
  4. Practise in the labOptionalTry a task from the module in the EDWartens Lab, in your browser.
  5. Sit the final examPass it once you have been through the modules.
  6. Certificate, if you want oneOptionalPaid, and only after you pass. Anyone can check it online.

EDWartens Lab

Practise this in the EDWartens Lab

Train a model on plant data, move the decision threshold and weigh the cost of errors in the Lab's AI workbench.

Works alongside Edge AI and TinyML for Industrial Sensing. The Lab is ai for industry practice in your browser: no install, no hardware, free to use. Sign in as a learner and it opens with your account.

Open the free lab See the Lab
AI for industry: Build AI models.

Levels to try with this course

Optional. Skip it whenever you like: it never blocks your progress or your certificate.

Why practise in a lab

Technical work is judged on what you can do: wiring, commissioning, finding a fault. Doing a task yourself teaches more than watching it a second time, and a lab lets you try it safely before you meet real equipment. It does not replace working on real hardware.

  • Practise safely, so a mistake costs nothing.
  • The lab checks your work like a site test, so you know when it is right.
  • Free, in your browser, with nothing to install.

Certificate

An optional certificate when you pass

Learning is free. Pass the final exam and you can have a certificate for £19, including VAT, with a number employers can check online. It records that you completed a free online course; it is not a regulated qualification or a CPD certificate.

  • Your name, the course, its study time and your final exam score
  • A unique certificate number and a QR code to its verification page
  • Issued by Wartens Ltd; add it to your LinkedIn profile in one click
  • Pay now or after you pass: it is issued the moment you pass the final

How the certificate works

Sample certificate for Edge AI and TinyML for Industrial Sensing

Credits

Who made the lessons

The video lessons belong to their creators. EDWartens wrote the study plan, notes, quizzes and the final exam.

  • Edge Impulse

    Shawn Hymel's Introduction to Embedded Machine Learning lessons on embedded and ML hardware, data collection, motion features, neural networks, evaluation, deployment to Arduino, audio classification and keyword spotting, plus What is Edge AI, connecting the ESP32, the C++ library and advanced anomaly detection

  • DigiKey

    Intro to TinyML Parts 1 and 2, the Edge AI Anomaly Detection series on data collection, features and deploying to an ESP32, wake word feature extraction, feature selection, STM32 X-CUBE-AI, and the Let's Talk Technical edge AI panel used as the final recap

  • IBM Technology

    the explainer on edge AI against distributed AI

  • EDGE AI FOUNDATION

    the tinyML Talks practical guide to neural network quantisation and the talk on managing tiny machine learning at industrial scale

  • Andreas Spiess

    the test of seven current sensors for microcontrollers and the ESP32 over-the-air update tutorial

  • Hardware.ai

    predictive maintenance with Arduino and Edge Impulse

  • Core Electronics

    YOLO object detection on the Raspberry Pi AI HAT with Python

  • Edje Electronics

    running YOLO26 detection models on the NVIDIA Jetson Orin Nano

  • Jeff Geerling

    the review of the Raspberry Pi AI HAT

  • Joyce Lin

    the edge AI comparison of Raspberry Pi, Hailo-8 and Jetson Orin Nano

  • Efficient NLP

    quantisation, pruning and distillation compared

  • Circuit Digest

    object detection on the ESP32-CAM with an Edge Impulse model

  • ThinkIOT

    the Wokwi ESP32 simulator with MQTT and a Node-RED dashboard

  • Learn Embedded Systems

    an ESP32 MQTT sensor node and a Raspberry Pi IoT server with InfluxDB, MQTT, Grafana, Node-RED and Docker

  • educ8s.tv

    the ESP32 deep sleep tutorial for low-power nodes

  • Evidently AI

    the explainer on data drift in machine learning monitoring

Questions

About this course

Who is this edge AI and TinyML course for?
It is for instrumentation, maintenance, IIoT and embedded engineers who want machine learning on the sensor itself, and for final-year electrical, electronics and instrumentation students anywhere. It suits anyone who looks after motors, fans and pumps and wants early warning of faults without streaming raw data to the cloud.
Do I need to buy hardware?
No. Every module can be done with a smartphone as the sensor in Edge Impulse, the Wokwi ESP32 simulator in the browser, and public datasets such as the CWRU bearing data and the MIMII machine sounds. If you want to build the real node, an ESP32 board with an ADXL345 accelerometer and a small desk fan is enough; a Raspberry Pi 5 with an AI HAT or a Jetson is optional for the vision module.
Is the course free to learn?
Yes. Every module, the notes, the worked problems, the project and the final assessment are free, and every tool used has a free route: the Edge Impulse Developer plan, Arduino IDE, Google Colab, Node-RED, Mosquitto and Wokwi. The certificate is optional.
What do I need to know first?
You should be able to read a datasheet and edit a short Arduino or Python program. Machine learning is taught from the level a tiny model needs; Machine Learning with Python or Predictive Maintenance with Machine Learning are good courses before or after this one, and Node-RED for Industrial IoT goes deeper into MQTT and dashboards.
Is Edge Impulse still free now that it is part of Qualcomm?
Yes, for learning. Edge Impulse joined Qualcomm (announced March 2025) and still supports boards from other makers. Its free Developer plan gave 3 private projects, 60 minutes of compute per job and GPU training when checked in October 2026, for learning, internal R&D and pre-production; a product shipped to customers needs the paid Enterprise plan. Read the current terms before building anything for production.
Can an edge AI node trip or stop a machine?
No. The course treats every edge AI output as an advisory alarm for people, work orders and history. Trips and safety functions need certified sensors and logic designed to standards such as IEC 61511 or IEC 62061, which are covered in Safety Instrumented Systems and SIL to IEC 61511.
How long does the course take?
About 18 hours of video, notes and practice at your own pace, of which about 10 hours is video. The optional vibration node project takes about 18 hours more.
What certificate do I get?
An EDWartens certificate of completion, issued when you finish the modules and pass the 15-question final at 60 percent, with a number anyone can verify on our site. It is not a certification from Edge Impulse, Qualcomm, Arduino, Espressif, Raspberry Pi or NVIDIA, none of whom is affiliated with EDWartens.

The Professional Module is five live days of Siemens PLC, HMI and SCADA with an instructor, then a resume session and a CPD certificate.

See the Professional Module