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Machine learning still needs data scientists to optimise results

Justin van der Lande Principal Analyst, Research

"Machine learning is not a 'one-size-fits-all' technology, but a growing library of technologies that need to be understood and deployed correctly to achieve meaningful results."

Machine learning (ML) can help communications service providers (CSPs) to manage the growing expense of creating and maintaining algorithms built by data scientists. The ability to allow machines to learn insights and relationships through the application of ML techniques means analytics can be applied to more use cases. In addition, ML is a key component in the creation of artificial intelligence, which enables applications to learn from their environments. However, ML is not simple and CSPs and vendors need to carefully select which ML algorithms are used for each use case.