Automated Machine Learning — An Overview

Author: Fatos Ismali

Since the dawn of the computer age, scientists and engineers have always wondered about infusing computers with the ability to learn, just like humans do. Alan Turing was amongst the first scientists to posit a theory of intelligence that envisaged computers to one day be able to reach a level of intelligence that aims to reach human parity. Since then a number of giant leaps have been made that have pushed the field of Machine Learning forward. We have seen Machine Learning in many cases beating or at least matching specific human cognitive faculties such as in the case of ResNet (a deep Residual Network architecture) surpassing human performance in image recognition, or Microsoft’s speech transcription system almost reaching human-level performance. One of the biggest benefits of Machine Learning is that it can be applied to almost any problem that humanity faces today. However, with that benefit, there are also challenges. Machine Learning algorithms need to be configured and tuned for every different real-world scenario. This makes it very manually intensive and takes a huge amount of time from a human supervising its development. This manual process is also error-prone, not efficient, and difficult to manage. Not to mention the scarcity of expertise out there to be able to configure and tune different types of algorithms. If the configuration, tuning, and model selection is automated, the deployment process will be made more efficient and humans can focus on the more important tasks such as model interpretability…