Machine learning is the future, but is not without its challenges, says STX Next

Krzysztof Sopyla, Head of Machine Learning and Data Engineering at STX Next, offers advice on addressing the complexities of undertaking a machine learning project

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Founded in 2005, STX Next is an international digital engineering consultancy focused on delivering data and AI solutions, integrated into modern Cloud infrastructure with exceptional UX and design. The business services a global customer base through a flexible, multi-location nearshoring model from two delivery centers in Poland and Mexico, supported by a deep, educated talent pool. With nearly 20 years of expertise and a talented team of 500 professionals, STX has successfully delivered over 1,000 projects.

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The popularity of artificial intelligence (AI) and machine learning (ML) has skyrocketed in recent years. In fact, STX Next’s 2021 CTO report found that around two-thirds of businesses have now implemented ML in some form and a further three-quarters of CTOs see it as the most likely technology to come to prominence in the next two years.

According to Krzysztof Sopyla, Head of Machine Learning and Data Engineering at STX Next, maximising the potential of machine learning means being fully aware of the complexity and unique challenges that come with these types of projects, and taking the necessary steps to overcome them.

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