Data & Analytics
The theory underpinning the advances in machine learning has existed for decades, if not centuries. As early as the 1960's, the term 'machine learning' was coined as a marketing sleight-of-hand to attract clients and employees. But the application of this field, which is a repackaging of pattern recognition through the lens of statistics and mathematics further implemented through computer science, took off once the two magic factors listed above materialized concurrently.
Even after use cases rapidly started being powered by machine learning, it was common to see models developed but not deployed. A VentureBeat report confirmed this; almost 90% of machine learning models developed never make it to production. Accordingly, in the last 3 – 5 years, the focus has shifted to the engineering aspect of machine learning, i.e., deployment and maintenance through technology concepts such as DevOps CI / CD, leading to the portmanteau practice called MLOps.
Not to be outshone, the revenge of the machines followed soon; IoT devices and intelligent sensors became de rigueur in multiple industries to help dig even more value, leading to a shift from a humanist world to an increasingly techno-human world.
Our Data & Analytics Service line is cataloged into three sub-verticals catering to the full spectrum of the datascape, depending on your company and its maturity.
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