> For the complete documentation index, see [llms.txt](https://dev.tesa.or.th/tesa-developer/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://dev.tesa.or.th/tesa-developer/tesa-university-program/rt-thread-iot-os/university-program/edge-ai-workshop/ai-model-training-via-edge-impulse.md).

# AI Model Training via Edge Impulse

<figure><img src="/files/4XLVZY24SCgdz8RzrR7t" alt=""><figcaption></figcaption></figure>

## Edge Impulse

Edge AI is the development and deployment of artificial intelligence (AI) algorithms and programs on edge devices. It is a form of edge computing where data is analyzed and processed near where the data is generated or collected. Edge AI contrasts cloud-based AI, which involves data being transmitted across the internet to be processed on a remote server.

In machine learning (ML), data is fed into the training process. For supervised learning, the ground-truth labels are also provided along with each sample. The training algorithm automatically updates the parameters (also known as "weights") in the ML model. Most ML projects follow a similar flow when it comes to collecting data, examining that data, training an ML model, and deploying that model. Optimization can involve a number of processes that reduce the size and complexity of the ML model, such as pruning unimportant nodes from the neural network, quantizing operations to run more efficiently on low-end hardware, and compiling models to run on specialized hardware (e.g. GPUs and NPUs).

<figure><img src="/files/oCr80FiQKlffYqjyBrvi" alt=""><figcaption></figcaption></figure>

Edge Impulse is the leading edge AI platform for collecting data, training models, and deploying them to your edge computing devices. It provides an end-to-end framework that easily plugs into your edge MLOps workflow.