Many firms struggle to find data scientists who can find useful patterns in corporate data, so it will come as welcome news to some that machine learning platforms can fulfil the same role.
That's the view of Albert Hitchcock, CIO of publishing and education group Pearson, who told V3 sister site Computing this week that "the days of writing to a Hadoop database and having a data scientist write algorithms are rapidly disappearing".
"We just put the data into a machine learning platform, and it spots the patterns," he said.
This could be good news for companies that find it difficult to recruit data scientists, many of whom have been snapped up by large corporates such as Google and Amazon.
Indeed, V3 reported earlier this year about high-profile data scientist vacancies at Sainsbury's and Channel 4.
Pearson is in the middle of a large digital transformation project that includes discarding the majority of the group's on-premise hardware and moving wholesale to the cloud.
Part of the project is an ambitious drive to produce adaptive content on the web so that customers can see individually tailored information. This relies on machine learning to search for and display the right content for each customer.
"We're starting to meta-tag all the content so it can be chopped up into component parts and reassembled on the fly [for each user]," explained Hitchcock.
He likened it to the techniques used by firms such as Google in selecting which ad to display online based on its understanding of each user.
"But our use case is much richer. We're changing the nature of the content, so we need deeper analytics, and we use Pearson's unique intellectual property to modify that learning content on the fly," he said.
"I think we're seeing some really interesting things in machine learning. The platforms are teaching themselves how to come up with suggestions. It's exciting and frightening."
Hitchcock said that the company is likely to choose several machine learning platforms.
"We will probably in the future end up plugged into multiple machine learning environments, and we'll just choose whichever result is the best [in each case]," he said.
"So we'll consume various machine learning services, and choose the most accurate answer based on real-time feedback."
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