Exploring why ML development ended up being mostly model-centric
A short history lesson is in order to truly appreciate why a data-centric approach is the key to unlocking the full potential of ML.
The fields of data science and ML have achieved significant advancements since the earliest attempts to make electronic computers act intelligently. The intelligent tasks performed by most smartphones today were nearly unimaginable at the turn of the 21st century. Moreover, we are producing more data every single day than was created from the beginning of human civilization to the 21st century – and we’re doing so at an estimated growth rate of 23% per annum1.
Despite these incredible developments in technology and data volumes, some elements of data science are very old. Statistics and data analysis have been in use for centuries and the mathematical components of today’s ML models were mostly developed long before the advent of digital computers.
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