Tensorflow Lite for Microcontrollers Training Course


21 hours (usually 3 days including breaks)


  • C or C++ programming experience
  • A basic understanding of Python
  • A general understanding of embedded systems


  • Developers
  • Programmers
  • Data scientists with an interest in embedded systems development


TensorFlow Lite for Microcontrollers is a port of TensorFlow Lite designed to run machine learning models on microcontrollers and other devices with limited memory.

This instructor-led, live training (online or onsite) is aimed at engineers who wish to write, load and run machine learning models on very small embedded devices.

By the end of this training, participants will be able to:

  • Install TensorFlow Lite.
  • Load machine learning models onto an embedded device to enable it to detect speech, classify images, etc.
  • Add AI to hardware devices without relying on network connectivity.

Format of the Course

  • Interactive lecture and discussion.
  • Lots of exercises and practice.
  • Hands-on implementation in a live-lab environment.

Course Customization Options

  • To request a customized training for this course, please contact us to arrange.

Course Outline


  • Microcontroller vs Microprocessor
  • Microcontrollers designed for machine learning tasks

Overview of TensorFlow Lite Features

  • On-device machine learning inference
  • Solving network latency
  • Solving power constraints
  • Preserving privacy

Constraints of a Microcontroller

  • Energy consumption and size
  • Processing power, memory, and storage
  • Limited operations

Getting Started

  • Preparing the development environment
  • Running a simple Hello World on the Microcontroller

Creating an Audio Detection System

  • Obtaining a TensorFlow Model
  • Converting the Model to a TensorFlow Lite FlatBuffer

Serializing the Code

  • Converting the FlatBuffer to a C byte array

Working with Microcontroller’ss C++ Libraries

  • Coding the microcontroller
  • Collecting data
  • Running inference on the controller

Verifying the Results

  • Running a unit test to see the end-to-end workflow

Creating an Image Detection System

  • Classifying physical objects from image data
  • Creating TensorFlow model from scratch

Deploying an AI-enabled Device

  • Running inference on a microcontroller in the field


Summary and Conclusion

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