Machine Learning Resources for IoT and Embedded Developers

Get essential building blocks to streamline your ML workflows, and gain valuable insights from ML developers as they share their development journey and lessons learned. This curated list helps you build, train, test, and deploy ML at the edge. 

Existing ML Projects

Explore AI and ML projects and case studies that solve real-life problems. 


Object Detection with Grovety 3 min Case study sharing the architecture, components, neural networks, and tools chosen for AI-powered trail camera.
Natural Language Processing with Sensory 3 min Case study showing how Sensory developed a voice assistant with natural language processing in a memory- and power-constrained consumer device.
Neural Network Biometric Models with TinyMLOps 25 min Video showing how Fortifyedge rapidly prototyped with its Tensorflow models deployed on Google Android and Wear OS to run ML workloads on device.
Setting a Wake Word Application 4 min Video on how to create a wake word application on the Raspberry Pi Pico.
Accelerate Inference and Optimize Performance with Arcturus and NXP 28 min Video on accelerating AI IoT development, with a demo of how ML DevOps can simplify deployment.
Building an IoT-enabled Artificial Nose Using TinyML 32 min Video about the journey to building an open source and open hardware DIY artificial nose, plus TinyML best practices.

AI and ML Development Solutions

Gain valuable insights and explore hardware, software, tools, and frameworks to streamline your ML workflows.  


End-to-End Neural Network Model Development Tutorial 10 min Video to learn how to train and deploy a neural network from a Jupyter notebook using Google Colab.
Adventures in Debugging 30 min Video exploring troubleshooting and debugging tips, including GDB and python, signal probing techniques, and tracing.
Securing Deployment of AI to Constrained Devices 28 min Video on using open source and open standards to secure AI models during deployment.
Going Cloudless: AI at the Edge 58 min Video exploring the potential of edge AI and sharing technologies for cloudless edge AI solutions for vision, object detection, and vital signs prediction.
Easy TinyML: Practical Examples to Get Started Today 1 hr Video exploring the potential of edge AI and sharing technologies for cloudless edge AI solutions for vision, object detection, and vital signs prediction.
Kickstart Your ML Journey

Do You Have the Right IP and Tools?

Access resources and step-by-step guidance to take your ML projects from inception to reality.

arm MDK

Arm Keil MDK

Development solution for Arm-based MCUs, including components needed to create, build, and debug embedded applications.

Vela Compiler

Vela Compiler

Open-source Python tool to optimize a neural network model that can run on an embedded system containing an Ethos-U NPU.

ML Inference Advisor

Machine Learning Inference Advisor

Optimize neural network models for efficient inference on Arm that provides performance analysis and actionable advice.

arm Cortex-M

Arm Cortex-M

Processors addressing performance, power, and cost requirements.

arm Ethos

Arm Ethos NPUs

Maximize performance, power, and efficiency for ML on the edge.

Usage Guides and Support


ML Integration Guide: Arm Ethos and Arm Cortex-M 1 hr Guide for and Cortex-M processors, tools to support software development, and the available evaluation paths.
Bringing ML and DSP to Constrained Devices with Arm Helium Technology 1 hr Video on how Helium technology can bring ML and DSP capabilities to constrained devices with a demo of the tools and libraries to target Helium.
Navigate Machine Learning with Arm Ethos-U NPUs 10 min Learning Path to help you get started with Cortex-M and Ethos-U ML application development.
Build and Run Arm ML Evaluation Kit Examples 30 min Learning Path to build examples from machine learning evaluation kit and run the examples on Corstone-300 FVP or Arm Virtual Hardware (AVH)
Build and Run a Letter Recognition NN Model using TensorFlow 1 hr Learning Path to build a letter recognition neural network model using TensorFlow and run the model on an STM32L4 board.
Build and Run an Image Classification NN Model 1 hr Learning Path to build a convolution neural network model for image classification and run the model on an STM32L4 board.
Deploy PaddlePaddle on Arm Cortex-M with Arm Virtual Hardware 30 min Learning Path showing how to export the Paddle inference model, compile the model with TVMC, and deploy on AVH.

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