TinyML Tutorial 3.5: TensorFlow Lite Micro

Welcome to the TinyML Tutorial 3.5 on TensorFlow Lite Micro. In this tutorial, we will be diving into the world of TinyML and exploring how to use TensorFlow Lite Micro to deploy machine learning models on microcontrollers.

What is TensorFlow Lite Micro?

TensorFlow Lite Micro is a lightweight version of TensorFlow specifically designed for microcontrollers and other resource-constrained devices. It allows developers to run machine learning models on these devices, enabling them to create intelligent and responsive edge-based applications.

Getting Started with TensorFlow Lite Micro

Before we can begin using TensorFlow Lite Micro, we need to set up our development environment. This typically involves installing the necessary packages and tools, such as the TensorFlow Lite Micro library, a compatible compiler, and a development board or microcontroller.

Creating and Deploying a Simple Machine Learning Model

Once our development environment is set up, we can start creating and deploying our machine learning model. This may involve training a model using a high-level machine learning framework, such as TensorFlow or Keras, and then converting it to a format compatible with TensorFlow Lite Micro.

Running Inference on a Microcontroller

After the model has been converted, we can deploy it to our microcontroller and run inference on real-world data. This allows us to see how our machine learning model performs in a real-world, edge-based scenario.

Conclusion

In this tutorial, we have explored TensorFlow Lite Micro and how it can be used to deploy machine learning models on microcontrollers. By leveraging the power of TinyML, developers can create intelligent and responsive edge-based applications that are capable of running machine learning models on resource-constrained devices.