, and especially , have turned out to be incredibly useful in the right hands, as well as incredibly demanding of computer hardware. The boom in availability of high-end GPGPUs (general purpose graphics processing units), FPGAs (field-programmable gate arrays), and custom chips such as Google’s Tensor Processing Unit (TPU) isn’t an accident, nor is their appearance on cloud services.
But finding the right hands? There’s the rub—or is it? There is certainly a perceived dearth of qualified data scientists and machine learning programmers. Whether there’s a real lack or not depends on whether the typical corporate hiring process for data scientists and developers makes sense. I would argue that the hiring process is deeply flawed in most organizations.
If companies teamed up domain experts, statistics-literate analysts, SQL programmers, and machine learning programmers, rather than trying to find data scientists with Ph.D.s plus 20 years of experience who were under 39, they would be able to staff up. Further, if they made use of a tool such as H2O.ai’s Driverless AI, which automates a significant portion of the machine learning process, they could make these teams dramatically more efficient.
As we’ll see, Driverless AI is an automatically driven machine learning system that is able to create and train surprisingly good models in a surprisingly short time, without requiring data science expertise. However, while Driverless AI reduces the level of machine learning, feature engineering, and statistical expertise required, it doesn’t eliminate the need to understand your data and the statistical and machine learning algorithms you’re applying to it.