With the war on AI talent heating up, the new “unicorns” of Silicon Valley are high-performing data scientists. Although as recently as 2015 there was a surplus of data scientists, in the most recent quarter there was a 150,000 deficit. This quant crunch will only grow deeper as the gap between the demand for these experts in developing machine learning models is not met with the supply from graduate programs.
How do leading companies take steps to mitigate the damage of the quant crunch on their ability to earn a return on machine learning and AI investments? They empower the experts that they do have with a combination of tools and techniques that automate as much of the tedious components of the modeling process as possible.
It is a relatively simple formula: automate tasks that do not benefit from domain expertise, thereby freeing your team up to spend time on the tasks that do. Below is a framework for considering which tasks are automatable and the beginning of a playbook for how to do so most efficiently and effectively.
Source : Datanami