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Machine Learning (ML) requires data on which to train and iterate. Making use of data for ML also requires a basic understanding of what is in the training data, which isn’t always an easy problem to solve.
Notably, there is a real challenge with unstructured data, which by definition has no structure to help organize the data so that it can be useful for ML and business operations. It’s a dilemma that Vikram Chatterji saw, time and again, during his tenure working as a project management lead for cloud artificial intelligence (AI) at Google.
In large companies across multiple sectors including financial services and retail, Chatterji and his colleagues kept seeing vast volumes of unstructured data including text, images and audio that were just lying around. The companies kept asking him how they could leverage that unstructured data to get insights. The answer that Chatterji gave was they could just use ML, but the simple answer was never really that simple.
“We realized very quickly that the ML model itself was something we just picked up off the shelf and it was very easy,” Chatterji told VentureBeat. “But the hardest part, comprising 80 to 90% of my data scientist job, was basically to kind of go in and look at the data and try to figure out what the erroneous data points are, how to clean it, how to make sure that it’s better the next time.”
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That realization led Chatterji and his cofounder …