Automated Deep Learning: Neural Architecture Search Is Not the End. (arXiv:2112.09245v2 [cs.LG] UPDATED)

Deep learning (DL) has proven to be a highly effective approach for
developing models in diverse contexts, including visual perception, speech
recognition, and machine translation. However, the end-to-end process for
applying DL is not trivial. It requires grappling with problem formulation and
context understanding, data engineering, model development, deployment,
continuous monitoring and maintenance, and so on. Moreover, each of these steps
typically relies heavily on humans, in terms of both knowledge and
interactions, which impedes the further advancement and democratization …

architecture arxiv deep learning learning neural architecture search search