There is something special about seeing an image that you have created on a display. That display could be demonstrating a simple, transparent image showcasing an image sensor’s capability. Alternatively, it could be implementing an advanced image … Read more
Machine Learning Developer
The Machine Learning Developer is specialized in integration of machine learning and Artificial Intelligence into tools and apps. S/He is able to develop, customize and optimize learning algorithms.
The main requirements of a Machine Learning Developer are:
deep knowledge of languages such as Python and C ++, experience in the implementation of algorithms in Computer Vision and Machine Learning (Deep Learning), knowledge of object- oriented programming, data-parallel processing and/or GPU programming with CUDA and OpenCL.
Find job offers in Job Search for Machine Learning Developer.
AI at the Edge of IoT (IoT Edge AI )
In the previous article in this series co-produced with Mouser Electronics, we saw four of the many use cases in which programmable logic plays a central role. Now, this article will consider IoT Edge AI, a subject that is currently very popular. … Read more
Programmable Logic: FPGA Internal and External Interfacing
In the third article in this series co-produced with Mouser Electronics, we saw how to simplify FPGA implementation tools' usage with user-friendly toolchains for design verification and programming file. Now, let’s see how FPGA Internal and External … Read more
Understanding Xilinx Design Tools
In the second article in this series co-produced with Mouser Electronics, we saw how to implement embedded processors with programmable logic devices. Now, let’s see how vendors have lowered programmable logic tools' learning curve. Design … Read more
Embedded Processing in Programmable Logic
In the first article in this series co-produced with Mouser Electronics, we explored the range of FPGA devices produced by Xilinx and discussed the benefits of adopting such a system for developers, engineers, and end-users alike. Now, let’s dig a … Read more
How Augmented Reality (AR) Enables Remote Troubleshooting
Thinking about a production chain within big industry, it is easy to imagine how many machines must work together to provide high productivity levels. In such contexts, a single faulty mechanism can have a huge impact on the whole chain, causing … Read more
FPGAs: What Do They Do, and Why Should You Use Them?
Digital processing technologies We live in an analogue world, but more and more, digital processing is changing the ways in which we interact with and experience the world. Whether through satellite navigation or autonomous vehicles, … Read more
How to Implement AI Predictive Maintenance on Edge Devices (Case Study)
Predictive maintenance helps anticipate when maintenance should be performed on machinery. In many industries, this approach uses AI and machine learning techniques, which in turn need to be run effectively on specific devices. Recently, Capgemini … Read more
How to Optimise Your IoT Device’s Power Consumption
Long battery life is a frequent concern of people purchasing Internet of Things (IoT) products — especially those for remote or harsh environments. That’s an understandable concern. Excessive power consumption could negatively impact the … Read more
How to Implement Data Version Control and Improve Machine Learning Outcomes
It may surprise you to hear this, but machine learning is in something of a crisis. In recent years, machine learning researchers have found it increasingly difficult to reproduce the findings made by algorithms. A key problem that has been … Read more
The State of AI in 2021
We know that technology tends to move in hype cycles, where certain topics and terminology are bandied around in press releases, news articles, research, and funding announcements. These include Blockchain, ICO, IoT, as well as AI, Machine Learning … Read more
The Rise of Machine Learning at the Network Edge
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
AI Ladder: the IBM Approach to Artificial Intelligence
TIME TO UPGRADE YOUR APPLICATION ESTATE?Tackle Modernization and Cloudification - on November 19th 2020, 10:00 am (GMT+1)The Cloud and AI Forum is an opportunity to learn how Italian Executive & Technical Leaders introduced data efficiency, … Read more
Questions and Answers in Virtual Assistants
The typical customer care virtual assistant (aka chatbot) can answer simple questions and maybe even perform some actions. But we need chatbots that really help solve customer problems instead of disappointing them with a “Sorry, I don't know.” When … 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
Exploring LIME Explanations and the Mathematics Behind It
The expansion of artificial intelligence (AI) relies on trust. Users will reject machine learning (ML) systems they cannot trust. We will not trust decisions made by models that do not provide clear explanations. An AI system must provide clear … 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
Google AI Hub: what, why, how
Artificial intelligence (AI) and machine learning (ML) increasingly seem to be indispensable tools that developers need to be able to handle. There are many ways these tools can be put to use, applied to applications and products. In research and … Read more