The number of places where machine learning can be used effectively from both a cost perspective and a return perspective are small. They are usually tremendously large datasets at gigantic companies, and they probably have to build in house expertise because it's hard to package this up into a product and resell it for various industries, datasets, etc. Certainly something like autonomous driving needs machine learn…
“The number of places where machine learning can be used effectively from both a cost perspective and a return perspective are small.” Thankfully transfer learning and super convergence invalidates this claim. Using pre-trained models + specific training techniques significantly reduces the amount of data you need, your training time and the cost to create near state of the art models. Both Kaggle and google colab of…
I think this sentence invalidates your argument against:
“The number of places where machine learning can be used effectively from both a cost perspective and a return perspective are small.”
In a hobbyist world, free GPU time is an amazing thing, and you can do a lot of fun and rewarding projects using transfer learning and other techniques that avoid heavy engineering and data processing. In a business world, where your product must consistently and accurately perform well, problems that may be solved by ML need to be heavily scrutinized and researched, because for most problems there are cheaper, faster, more robust solutions. Free GPU time doesn't weigh in at this scale.