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Free Edge AI & On-Device AI Course: TinyML to Deployment

Master Edge AI and TinyML through a hands-on journey from embedded hardware setup to real-time inference. This free sequence covers model quantization, TensorFlow Lite optimization, and deployment on Arduino, ESP32, and STM32 devices for...

10 lessons 9h 27min total Free

Course overview

This course is a practical guide to the Edge AI workflow, moving from hardware selection to fully deployed machine learning models on microcontrollers. It begins with a detailed tutorial on an x86 development board before diving into the core of on-device inference with TensorFlow Lite model conversion and quantization. You will then explore the TinyML pipeline using Arduino, ESP32, and STM32 platforms, covering sensor data collection, neural network training, and C++ firmware integration. The curriculum also includes specialized sessions on deploying computer vision with CNNs and leveraging STM32’s X-CUBE-AI expansion pack for optimized performance.

Who this course is for

This course is for embedded systems engineers, IoT developers, and software programmers with basic Python or C++ knowledge who want to implement AI locally on hardware. It suits builders working with Arduino or ESP32 who need to run models without cloud connectivity. If you face challenges optimizing models under tight memory and processing constraints or want hands-on experience deploying real-time classification and object detection on microcontrollers, this sequence provides a direct, code-focused path.

How to study this sequence

Follow the lessons sequentially to build a complete understanding of the Edge AI deployment pipeline. Start with the x86 hardware tutorial to grasp embedded constraints, then move into TensorFlow Lite optimization and quantization. As you progress into TinyML, run the provided code examples on your own Arduino, ESP32, or STM32 boards. Pause to replicate the training workflows in Google Colab or Edge Impulse before attempting the firmware deployment steps. Ensure each model runs locally on your physical hardware to validate your understanding of inference latency and memory usage.

What you should be able to do

By completing this course, you will be able to collect sensor data, train a neural network, and deploy it as an optimized model directly onto a microcontroller. You will gain practical skill in using TensorFlow Lite, Edge Impulse, and STM32Cube.AI to implement real-time inference for applications like gesture recognition and object detection. The final result is a working embedded system performing AI tasks on-device, ready for you to adapt to custom hardware projects.

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What you learn in this Edge AI and On-Device AI course

This course organizes 10 Edge AI and On-Device AI lessons in a practical order, starting with the base and moving toward examples you can repeat in your own projects.

The main practice points are Understand embedded AI hardware architecture and x86 platform advantages for edge development, Set up and configure development boards for AI model deployment, Optimize and quantize AI models for edge computing constraints and Deploy end-to-end applications combining inference with sensor integration. Use this page to review the course proposal, check the key topics, and open the lessons in the recommended order.

What you will practice

  • Understand embedded AI hardware architecture and x86 platform advantages for edge development
  • Set up and configure development boards for AI model deployment
  • Optimize and quantize AI models for edge computing constraints
  • Deploy end-to-end applications combining inference with sensor integration
  • Measure and profile inference performance on resource-limited hardware
  • Understand edge computing fundamentals and their advantages over cloud-only architectures

Concepts covered

Edge AI Model Quantization Inference Optimization x86 Architecture Embedded Systems Hardware Constraints Model Deployment Edge Computing

Course lessons

10 tutoriais organizados em sequência.

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Course summary

This Edge AI and On-Device AI course brings together 10 lessons in a guided sequence. It starts with "BEST Embedded AI Hardware for Begineers! In-depth hands-on TUTORIAL" and moves step by step, so each lesson supports the next one and helps turn the topic into practice.

During the course, the practical focus is on Understand embedded AI hardware architecture and x86 platform advantages for edge development, Set up and configure development boards for AI model deployment, Optimize and quantize AI models for edge computing constraints and Deploy end-to-end applications combining inference with sensor integration. These points help you watch with a goal instead of treating the lesson as a loose introduction.

The course also introduces important ideas such as Edge AI, Model Quantization, Inference Optimization, x86 Architecture and Embedded Systems, which makes it easier to understand later tutorials and decide when each resource is useful.

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How to get more from this course

Who this Edge AI and On-Device AI course is for

This advanced course is for learners who already understand the fundamentals of Edge AI and On-Device AI and want to develop deeper technical or professional skills.

What you can do after finishing

By the end, you should be able to review the main idea of the course and practice Understand embedded AI hardware architecture and x86 platform advantages for edge development, Set up and configure development boards for AI model deployment, Optimize and quantize AI models for edge computing constraints and Deploy end-to-end applications combining inference with sensor integration with more confidence.

Recommended study order

Watch the 10 lessons in the order shown on this page. Pause after each lesson to repeat the examples and only move on when the previous step makes sense.

What to study next

After finishing the course, explore the Edge AI and On-Device AI topic to find related tutorials and keep studying with more context.

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Course questions

Is this Edge AI and On-Device AI course for beginners?

No. This course covers advanced Edge AI and On-Device AI topics and is best suited to learners who already understand the fundamentals.

How many lessons are in this course?

This course currently has 10 lessons and about 9h 27min in total.

What should I do after finishing the course?

The best next step is to explore the Edge AI and On-Device AI topic and choose a related tutorial that matches what you want to practice.

Is this course free?

Yes. This is a free course organized by CursoB so you can study Edge AI and On-Device AI in a structured sequence.

Do I need previous experience to follow the course?

Yes. You should already understand the fundamentals of Edge AI and On-Device AI before starting this advanced course.

Should I watch the lessons in order?

Yes. The recommended path is to watch the 10 lessons in the order shown, because each lesson helps prepare the next one.

Can I use this course for practical projects?

Yes. Use the course as a practical reference to train Understand embedded AI hardware architecture and x86 platform advantages for edge development, Set up and configure development boards for AI model deployment, Optimize and quantize AI models for edge computing constraints and Deploy end-to-end applications combining inference with sensor integration and adapt the examples to your own projects.

Does this course include a certificate?

CursoB stopped issuing certificates for its own courses in May 2026. CursoB no longer offers completion certificates. Courses and tutorials remain free to study. If content is hosted on another platform, any certificate depends exclusively on the original author or platform rules.

Are the lessons updated?

CursoB periodically reviews the Edge AI and On-Device AI catalog and may add, remove, or reorder lessons when better content is found.

Where can I find more content on this subject?

Explore the Edge AI and On-Device AI topic to find related tutorials and complementary courses.

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What each course lesson covers

Use these notes to understand the sequence before opening each lesson.

  1. 01 BEST Embedded AI Hardware for Begineers! In-depth hands-on TUTORIAL In this lesson, you practice Understand embedded AI hardware architecture and x86 platform advantages for edge development, Set up and configure development boards for AI model deployment and Optimize and quantize AI models for edge computing constraints.
  2. 02 TensorFlow Lite for Edge Devices – Tutorial In this lesson, you practice Understand edge computing fundamentals and their advantages over cloud-only architectures, Convert TensorFlow and Keras models to optimized TensorFlow Lite format and Apply quantization techniques to compress models while preserving accuracy.
  3. 03 Arduino Machine Learning Tutorial: Introduction to TinyML with Wio Terminal In this lesson, you practice Understand TinyML fundamentals and edge machine learning principles, Configure the Wio Terminal hardware and Arduino IDE development environment and Collect and preprocess sensor data using Edge Impulse.
  4. 04 Intro to TinyML Part 2: Deploying a TensorFlow Lite Model to Arduino | Digi-Key Electronics In this lesson, you practice Deploy TensorFlow Lite models to Arduino microcontrollers, Write Arduino sketches for machine learning inference and Visualize model predictions using Serial Plotter.
  5. 05 Intro to TinyML Part 1: Training a Neural Network for Arduino in TensorFlow | Digi-Key Electronics In this lesson, you practice Train a neural network using TensorFlow and Google Colab without local installation, Convert a full TensorFlow model to TensorFlow Lite format for embedded deployment and Generate C header files from trained models for Arduino integration.
  6. 06 TinyML Tutorial ESP32 In this lesson, you practice Understand TinyML fundamentals and edge computing benefits, Use Edge Impulse Studio for model creation and optimization and Integrate DHT22 sensors with ESP32-S3 microcontrollers.
  7. 07 Computer Vision with TinyML (Edge AI + CNNs) | Deploy Machine Learning on Microcontrollers Devices In this lesson, you practice Build computer vision applications that interpret images and video streams, Design and optimize Convolutional Neural Networks for embedded deployment and Train machine learning models and convert them for microcontroller execution.
  8. 08 TinyML: Getting Started with STM32 X-CUBE-AI | Digi-Key Electronics In this lesson, you practice Install and configure the X-CUBE-AI add-on package in STM32CubeIDE, Load and convert trained neural networks in TensorFlow Lite format for STM32 deployment and Use CubeMX to auto-generate embedded inference code and peripheral initialization.
  9. 09 TinyML: Getting Started with TensorFlow Lite for Microcontrollers | Digi-Key Electronics In this lesson, you practice Understand the complete TinyML deployment workflow from model training to embedded inference, Generate TensorFlow Lite for Microcontrollers source code using the Make build system and Integrate TensorFlow Lite libraries into STM32CubeIDE embedded projects.
  10. 10 Adding AI to your ESP32 is Easier than You Think! In this lesson, you practice Implement object detection and image recognition on ESP32 microcontrollers, Deploy machine learning models using Edge Impulse and TensorFlow Lite for Microcontrollers and Build keyword spotting systems for voice-activated embedded applications.