H elix

Data application developers and data scientists spend an inordinate amount of time iterating on machine learning (ML) workflows---by modifying the data pre-processing, model training, and postprocessing steps---via trial-and-error to achieve the desired model performance. Existing work on accelerating machine learning focuses on speeding up one-shot execution of workflows, failing to address the incremental and dynamic nature of typical ML development. We propose H elix , a declarative machine learning system that accelerates iterative development by optimizing workflow execution end-to-end and across iterations. H elix minimizes the runtime per iteration via program analysis and intelligent reuse of previous results, which are selectively materialized---trading off the cost of materialization for potential future benefits---to speed up future iterations. Additionally, H elix offers a graphical interface to visualize workflow DAGs and compare versions to facilitate iterative development. Through two ML applications, in classification and in structured prediction, attendees will experience the succinctness of H elix 's programming interface and the speed and ease of iterative development using H elix . In our evaluations, H elix achieved up to an order of magnitude reduction in cumulative run time compared to state-of-the-art machine learning tools.