Top trends in AI for data scientists

AI and data science are ever-evolving spaces which will continue to see vast amounts of change in a short period of time. Keeping up with new trends is key, so let’s take a look at some key AI trends to keep an eye on.

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1. The artisanal days of data science are over

With more businesses recognising the benefits of data science, it is now becoming a much more industrial activity. So, the process of producing data models has shifted significantly, with more businesses looking to invest in processes, methodologies and platforms that will boost productivity on a large scale.

Forming a partnership with a data analysis company, such as, can support the development of robust data models on an industrial scale, particularly when it comes to ensuring that no potentially valuable trends or patterns are accidentally overlooked.

2. Generative AI must deliver tangible value

Generative AI has barely left news headlines recently but although it is undoubtedly a hot topic of conversation, it cannot be said to have delivered much value yet. Driving tangible value from generative AI will require a robust data strategy, so data scientists have the potential here to help ensure that AI is integrated into wider technological infrastructures and is best placed to start driving beneficial outcomes.

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For some context here, the fact that Google, with its vast data science resources, has successfully managed to build an AI model with the ability to predict weather-related catastrophes ahead of every weather agency across the globe highlights just how critical data scientists are when it comes to utilising AI to drive actual value.

3. Data leaders are becoming more integrated into companies

While data scientists were once extremely independent, many businesses are starting to recognise that their technological growth is being hindered by a distinct absence of collaboration between different departments. This means that, moving forwards, data leaders will need to develop a business-focused mindset in order to communicate the benefits of data strategies with senior managers whilst also being able to use their knowledge to develop systems that will maximise the outcomes of those strategies.

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