Skip to main content
Learn how to build full machine learning pipelines — from raw data to deployed model — using Edge Impulse. These tutorials walk you through every step of the process for your use case or domain: collecting data, designing an impulse, training a model, testing results, and deploying to real hardware.

​
🖼️ Image tutorials

Explore visual ML applications including image classification and object detection.

​
🎧 Audio tutorials

Build models that understand sound — from detecting specific keywords to recognizing environmental noises.

​
📈 Time-series tutorials

Learn how to use motion, sensor, and other temporal data to detect activity, monitor environments, and identify anomalies.

​
🤖 AI agent tutorials

Build Edge Impulse projects using natural language commands with AI coding agents.

​
⚙️ Hardware-specific tutorials

Deploy real applications on supported embedded hardware platforms and development kits.

​
🚀 What you’ll learn

By completing these tutorials, you’ll learn how to:
  • Collect and label data from devices and sensors
  • Design and configure impulses tailored to your data type
  • Train and evaluate models using Edge Impulse Studio
  • Optimize models for real-time edge deployment
  • Deploy and run models on actual embedded hardware
Whether you’re a beginner exploring your first model or an expert integrating with specific hardware, these guides will help you go from idea to deployment, end-to-end.