04 May, 2020
Classification of objects to defined buckets is a very natural human tendency. It helps us understand information better by making us think of a fewer groups, rather than many individual pieces of data. It’s therefore not surprising that classification is one of the first problems taken up by data scientists when machine learning started getting mainstream.
In best of cases, the problem of accuracy in classification is real, driven by a need for copious amounts of clean and labelled data that is required for an AI engine to get trained on. This in itself is a challenge as it gets exacerbated by the need for large volumes of data and expensive computing infrastructure to process within a meaningful time.
In enterprise context, further complications are introduced by information overload coming from diverse channels and entry points into the enterprise – contact centers, dealers, web traffic, and increasingly through social media. Enormity of data, created via multitude of languages in global enterprises, starts resembling the Biblical Tower of Babel more than anything else.
Are enterprises therefore forced to choose between poor accuracy that renders this whole exercise less than useful, spend enormous time and money to harmonize and clean things up or silently skyrocketing of infrastructure costs to support compute hungry algorithms?
Sainapse, purpose built for enterprise use cases, addresses this issue head on by taking away usual AI adoption concerns. Talk to us and experience how accuracy and cost of implementation are not inversely proportional