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.

翻译：近年来，软件与硬件技术的进步使得AI/ML模型能够应用于日常场景，显著提升了服务质量。然而，针对特定应用寻找合适的AI/ML模型仍是一项复杂且高成本的工作，涉及数据预处理、特征工程、特征选择及模型调优等多个相互关联步骤（即流水线）的生成、训练与评估。这些流水线结构复杂，端到端执行在计算资源和时间上代价高昂，且每个步骤都关联着超参数。AutoML系统虽能自动化搜索这些超参数，但由于依赖优化流水线末端输出，运行速度缓慢。我们提出eTOP框架，该框架可部署于任意AutoML系统之上，自主决策流水线是完整执行还是提前终止于中间步骤。在26个基准数据集上的实验评估表明，将eTOP与MLBox4集成后，AutoML系统的训练时间较基线MLBox最高可降低40倍。