"even for highly technical aspects like deep learning, fast.ai has shown that people with 1-year of coding experience can become world-class deep learning practitioners"
Yes, any Joe can train deep network with dozen lines of Keras. Sure, your startup can use off-the self models and tweak it a bit... That doesn't make you "world-class deep learning practitioner". If you are designing the network for new problem domain, there are thousands of decisions to make - everything from hyperparameters to network architecture to distributions in data. Making these decisions without having developed lots of intuition and good foundations is very hard. I often find usual developers without ML background and experience completely lost in these cases. Debugging a model that doesn't work is super hard. There are no IDEs, no breakpoints, no watches and in fact not even error messages. Its purely statistical debugging and probabilistic fixes.
"even for highly technical aspects like deep learning, fast.ai has shown that people with 1-year of coding experience can become world-class deep learning practitioners"
Yes, any Joe can train deep network with dozen lines of Keras. Sure, your startup can use off-the self models and tweak it a bit... That doesn't make you "world-class deep learning practitioner". If you are designing the network for new problem domain, there are thousands of decisions to make - everything from hyperparameters to network architecture to distributions in data. Making these decisions without having developed lots of intuition and good foundations is very hard. I often find usual developers without ML background and experience completely lost in these cases. Debugging a model that doesn't work is super hard. There are no IDEs, no breakpoints, no watches and in fact not even error messages. Its purely statistical debugging and probabilistic fixes.