Welcome to Edge Impulse! We enable developers to create the next generation of intelligent device solutions with embedded Machine Learning. In the documentation you'll find user guides, tutorials and API documentation. For support, visit the forums.
If you're new to the idea of embedded machine learning, or machine learning in general, you may enjoy our quick guide: What is embedded ML, anyway?
Follow these three steps to build your first embedded Machine Learning model - no worries, you can use almost any device to get started.
- 1.You'll need some data:
- If you have an existing development board or device, you can collect data with a few lines of code using the Data forwarder or the Edge Impulse for Linux SDK.
- If you want to collect live data from a supported development kit, select your board from the list of fully supported development boards and follow the instructions to connect your board to edge impulse.
- 2.Try the tutorials on continuous motion recognition, responding to your voice, recognizing sounds from audio, adding sight to your sensors or object detection. These will let you build machine learning models that detect things in your home or office.
- 3.After training your model you can run your model on your device:
- If you want to integrate the model with your own firmware or project you can export your complete model (including all signal processing code and machine learning models) to a C++ or Arduino library with no external dependencies (open source and royalty-free), see Running your impulse locally.
- If you have a fully supported development board (or your mobile phone) you can build new firmware - which includes your model - directly from the UI. It doesn't get easier than that!
We have some great tutorials, but you have full freedom in the models that you design in Edge Impulse. You can plug in new signal processing blocks, and completely new neural networks. See Building custom processing blocks and Bring your own model.
You can access any feature in the Edge Impulse Studio through the Edge Impulse API. We also have the Ingestion service if you want to send data directly, and we have an open Remote management protocol to control devices from the Studio.
For startups and enterprises looking to scale edge ML algorithm development from prototype to production, we offer an enterprise-grade version of our platform. This includes all of the tools needed to go from data collection to model deployment, such as a robust dataset builder to future-proof your data, integrations with all major cloud vendors, dedicated technical support, custom DSP and ML capabilities, and full access to the Edge Impulse APIs to automate your algorithm development.