"The cloud is all about democratising IT," said Narin, explaining not all businesses that stand to benefit from machine learning solutions have the resources – for example the ongoing presence of highly-skilled engineers – required to build, maintain and scale these solutions themselves.
Machine learning can be particularly useful for extracting qualitative data from datasets too large for people to manually sort through, Narin explained. For example, it can be used to extract and aggregate sentiment from a large collection of reviews to determine the main reasons a particular product is getting mostly positive or mostly negative feedback, he noted.
It can also be used to flag language patterns pointing to potentially fraudulent orders, or counterfeit or contraband products, on an online sales platform, he said.
In addition, because machine learning enables the prediction of future outcomes in the absence of certain datasets, it can be used to predict sales trends for products with no existing sales data, said Narin. For example, it could use a new product's data to link it to other products and use their sales trends to estimate how much it will sell and over what time period, he offered.
Machine learning as a service helps businesses – especially SMEs – not to be held back by their lack of resources, in embracing the benefits it brings, he summarised.
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