AI Model Training via Edge Impulse

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).

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.