> data scientists said they spent 65% of their time using relational datasets vs 18% for images
It will be interesting to see the trend over years. One year doesn't say anything about the trend in the industry.
It will be interesting to see the trend over years. One year doesn't say anything about the trend in the industry.
Part of the reason is that use cases are driven by limited definitions of ML or "AI" -- AutoML for example note they are "Making AI accessible to every business..." but they are just managing a small part of one type of ML (images with conv nets and res nets.) A senior exec who reads this might develop a narrow view of what AI is.
A general annoyance is how obvious techniques like regression, tree models, Bayesian models, etc on tabular data are so ignored while everyone gets hyper-obsessed over GANs or whatever. Almost 90% of low-hanging-fruit I see can be captured with simple classic ML applied to tabular data.