eTOP: Early Termination of Pipelines for Faster Training of AutoML Systems
This addresses the high computational cost and time for AutoML users, offering a significant speed-up but is incremental as it builds on existing AutoML systems.
The paper tackles the slow training time of AutoML systems by proposing the eTOP framework, which decides whether to execute pipelines fully or terminate early, achieving up to 40x faster training than baseline MLBox on 26 benchmark datasets.
Recent advancements in software and hardware technologies have enabled the use of AI/ML models in everyday applications has significantly improved the quality of service rendered. However, for a given application, finding the right AI/ML model is a complex and costly process, that involves the generation, training, and evaluation of multiple interlinked steps (called pipelines), such as data pre-processing, feature engineering, selection, and model tuning. These pipelines are complex (in structure) and costly (both in compute resource and time) to execute end-to-end, with a hyper-parameter associated with each step. AutoML systems automate the search of these hyper-parameters but are slow, as they rely on optimizing the pipeline's end output. We propose the eTOP Framework which works on top of any AutoML system and decides whether or not to execute the pipeline to the end or terminate at an intermediate step. Experimental evaluation on 26 benchmark datasets and integration of eTOPwith MLBox4 reduces the training time of the AutoML system upto 40x than baseline MLBox.