When it comes to Industrial AI, the capabilities afforded by machines that can learn, predict and improve what we do are seemingly endless. Like all amazing new technolgy though – there’s a catch. We need to be aware of the data, bias and technical limitations – and the role that humans still have to play, in particular data scientists – to make sure we get the most out of this transformatie new technology.
The growth of AI and data science as critical innovation tools go hand in hand. The limitations of AI are where data scientists are needed most – to step in and handle the technical and data problems that AI can’t. It’s no wonder LinkedIn ranked ‘Data Scientist’ as the most promising role of the year in 2019..
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Read ArticleHow to confidently move from proof-of-concept to enterprise-wide AI adoption
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The efficient deployment, continuous retraining of models with live data and monitoring of model accuracy falls under the categorisation called MLOps. As businesses have hundreds and even.
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