Over the past few months, I have shown you why machine learning at the network edge is so essential. In this final article, we look back at why machine learning is needed and look at some of the real-world cases where it has already been … Read more
The Future of Machine Learning at the Edge
Over the last few months, I have shown you how Machine Learning at the edge is improving our lives. I also shared some practical examples to give you a chance to try it for yourselves. In this article, I look at the current limitations of edge ML and … Read more
Voice Control: Building Your Voice Assistant
Voice control was the stuff of science fiction throughout the 20th Century. But in the last two decades, voice control has entered the mainstream. Voice assistants like Siri and Alexa are embedded in home devices, headphones, and even … Read more
Seeing Is Believing: Image Recognition on a €10 MCU
This series is exploring the rationale for moving machine learning to the network edge. This article looks in more detail at image recognition, one of the prime use cases for ML at the edge. As explained in previous articles, there are many use … Read more
ML at the Edge: a Practical Example
Machine learning is the primary methodology for delivering AI applications. In previous articles, I discussed the main reasons behind moving machine learning to the network edge. These include the need for real-time performance, security … Read more
Getting started with edge machine learning
In this six-article series from Mouser Electronics, we explore why AI is moving to the network edge and the technology that’s making it possible. The first article examined why AI needs to move to the edge. Here, we dive in to the hardware and tools t … Read more
The cutting edge of real-time AI
Machine learning is traditionally processor-intensive; ML algorithms require large numbers of parallel operations. As a result, the models usually run in data centres at the core of the network. However, this has a direct impact in terms of latency, … Read more
Understanding AI: Training
Artificial intelligence (AI) and machine learning are terms informally used to mean a machine’s ability to mimic human cognitive functions, such as perceiving, reasoning and problem-solving. With human-like abilities to “think,” AI is taking on diver … Read more
Understanding AI: Inference
Inferencing is the second phase of machine learning, following on from the initial training phase. During the training phase, the algorithm generates a new model or repurposes a pre-trained model for a specific application and helps the model learn … Read more