All Case Studies Design Development Interviews Machine Learning Project Management
The power and potential of machine learning (ML) to bring huge benefits and new capabilities to mobile and web applications is remarkable. Today, more and more businesses and app developers are opting to build machine learning into their products to the extent that this once avant-garde technology is now becoming decidedly mainstream.
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Machine learning is one of the strongest trends in today's tech scene. A recent survey revealed that 61% of organizations picked machine learning as their most critical data initiative for 2018.
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Both artificial intelligence and machine learning are hot buzzwords right now. It's not surprising at all, since that’s exactly what modern technologies, such as: virtual agents, decision management, and even content creation, are starting to rely on.
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The infrastructure in cities of all sizes is evolving at a neck-breaking pace. New constructions are being built, and old ones are constantly being renovated, which requires heavy equipment such as diggers or cranes. I can imagine it might be a bit frustrating that every single photo from your city break is “embellished” with several cranes hanging above the horizon. The Sagrada Familia in Barcelona is a perfect example – do we really have to wait another ten years or so to get an Instagram-winning shot without any distractions in the background?
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Introducing Machine Learning Practice at Netguru

We’re happy to announce that we’ve kickstarted Machine Learning practice at Netguru. Matthew Opala, who became our ML Tech Lead, has dynamically grown a team of top experts in the industry.
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Over five hundred images, three days, and one team to release the first version of the application that will make our cities look prettier on photographs. Netguru and MicroscopeIT joined forces to run in an AI/ML-focused hackfest. The result? An app that can automatically remove all those pesky cranes from your cityscape pictures with a single click of a button, all thanks to Deep Learning algorithms. If you've ever been to London or Warsaw, you'll know that cranes are everywhere! Even though the app has very limited use (let's be honest, you don't actually get so many cranes in pictures, do you?), it lays a solid base for co-operation on future Machine Learning projects between the two companies.
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For a casual observer, the fintech scene looks like a uniform rock: it is all about digital banking alternatives (such as N26, Revolut, Tandem, Yoyo Wallet or Monzo). As in any industry, the same logic applies here - companies with a B2C business model attract customers the fastest, raise the largest investment rounds and, in the end, grab the most headlines.
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Machine learning applications can bring you more clients, increase sales and reduce business costs. However, if not used properly, they may lead to customer outflow, money loss and reputation damage. When should you use ML in your business and when is it better to stick with traditional computing methods?
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Machine learning models help businesses understand their data better, make more accurate predictions, and accelerate insights across the entire organisation. Amazon, Google, IBM, and Microsoft are the four largest players on the cloud scene, and they have all recently added some machine learning tools to help businesses take advantage of machine learning models for handling their data. But which solution is the right one for your business?
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Machine Learning is a trending field of Computer Science turning computer’s computations into a new level and giving a number of unique opportunities. It’s getting more and more popular, and it’s common for modern web application as well as services, such as Netflix, Spotify, Amazon.com and Facebook. Machine Learning is a good solution for apps based on recommendations or some kind of predictions. If you want to build such apps, you will need an efficient backend technology to support it. Is Ruby on Rails the right choice?
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