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At a very high level, AutoML is about using machine learning techniques to automatically do machine learning. Or in other words, it means automating the process of applying machine learning. Early on, AutoML was almost exclusively used for the automatic selection of the best-performing algorithms for a given task and for tuning the hyperparameters of said algorithms.
Yet AutoML can have a broader scope with later versions of auto-sklearn and tpot (and has). Its development has spurred the application of automation to the whole data-to-insights pipeline, from cleaning the data to tuning algorithms through feature selection and feature creation, even operationalization. At this larger scale, it’s no longer AutoML, but augmented analytics. Today, automated analytics can add efficiency to large swaths of the data pipeline, with the potential to impact the entire process and influence the structure of data teams long term.
“By 2025, 50% of data scientist activities will be automated by AI, easing the acute talent shortage.”
– Gartner, How Augmented Machine Learning Is Democratizing Data Science; Jim Hare, Carlie Idoine, Peter Krensky, 29 August 2019 (Report available to Gartner subscribers)