Automated Machine Leaning (AutoML) refers to systems that build machine learning models with some degree less manual coding than a data science programmer would do building models from scratch.
At Squark, AutoML means absolutely no coding or scripting of any kind. This is the strongest definition of AutoML. All of the steps in making predictions with machine learning models – import of training and production data, variable identification, feature engineering, classification or regression algorithm selection, hyperparameter tuning, leaderboard explanation, variable importance listing, and export of prediction data set – through a SaaS, point-and-click interface.
Various other implementations of machine learning are dubbed AutoML, but actually require extensive knowledge of data science and programming. For example, you may need to select algorithm type, pick hyperparameter ranges, launch from a Jupyter notebook, know Python, or use other processes that are not familiar.