Dive into AI's competitive edge in materials trading with Boldare's Head of AI Solutions, with practice and without technical jargon.

At Boldare, we are passionate about the services we provide and we love it when somebody notices. So, we’re delighted to announce that Boldare features on Clutch.co’s list of “2019 Leading Developers Across a Variety of Technology Focus Areas“ in the “Machine Learning Companies“ subcategory.

In the AI era everything is data and every kind of data can be processed and analyzed to produce a generalized model of its inner relationships. It really doesn’t matter if it’s text, picture, sound, sensor readings, video… there are machine learning and deep learning models able to handle the vast majority of data types.

Because of the rapid progress of machine learning over the last few years, expectations of what it can achieve are often not in line with reality. It’s still a relatively immature technology that doesn’t just work ‘out of the box’. Every model is unique, and must be trained, which involves a lot of experimentation.

When I told a fellow developer I was writing an article about the applications of Python, he told me: `It would be easier to list areas where Python doesn't apply`. I can't disagree with that. Python is used so widely nowadays, you can find it almost everywhere and that makes it a really convenient language to learn.

What did the last 12 months look like and what are we especially proud of? To answer that, we have prepared ten graphics illustrating Boldare’s most significant achievements in the past year.

One of the critical issues for the wind energy industry is the maintenance of wind farms, including component failures and replacement. The scale of this problem is significant. Maintenance operations can constitute approximately 20-25% of the total levelized cost per kWh, and each failure can cost up to $250,000 to resolve. Reactive maintenance is no longer a solution.

Artificial intelligence will soon become universal. Currently, investment rates are high but complete, tested products in operation are still relatively unusual. That said, a report from McKinsey clearly indicates that we are at a tipping point and AI and machine learning are set to be a dominant theme in tomorrow’s technology.

The question is - can a JavaScript developer, aware of the pace of progress and the complexity of this technology, start creating simple or even advanced neural networks? Especially if he or she has never had anything to do with them before, specializing instead in web applications? In this article I’m going to show you how to create a network using JavaScript in just 5 minutes.

Any company wanting to cash on the machine learning wave will have to act fast. But to do so, specific know-how is needed in the shape of IT professionals capable of developing and implementing solutions based on machine learning. And this is where the problems start because those specialists are not cheap.