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Recent advancements in software and hardware technologies have enabled theuse of AI/ML models in everyday applications has significantly improved thequality of service rendered. However, for a given application, finding theright AI/ML model is a complex and costly process, that involves thegeneration, training, and evaluation of multiple interlinked steps (calledpipelines), such as data pre-processing, feature engineering, selection, andmodel tuning. These pipelines are complex (in structure) and costly (both incompute resource and time) to execute end-to-end, with a hyper-parameterassociated with each step. AutoML systems automate the search of thesehyper-parameters but are slow, as they rely on optimizing the pipeline's endoutput. We propose the eTOP Framework which works on top of any AutoML systemand decides whether or not to execute the pipeline to the end or terminate atan intermediate step. Experimental evaluation on 26 benchmark datasets andintegration of eTOPwith MLBox4 reduces the training time of the AutoML systemupto 40x than baseline MLBox.

eTOP: Early Termination of Pipelines for Faster Training of AutoML Systems