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.

Machine learning workflow development is a process of trial-and-error: developers iterate on workflows by testing out small modifications until the desired accuracy is achieved. Unfortunately, existing machine learning systems focus narrowly on model training---a small fraction of the overall development time---and neglect to address iterative development. We propose H elix , a machine learning system that optimizes the execution across iterations ---intelligently caching and reusing, or recomputing intermediates as appropriate. H elix captures a wide variety of application needs within its Scala DSL, with succinct syntax defining unified processes for data preprocessing, model specification, and learning. We demonstrate that the reuse problem can be cast as a M ax -F low problem, while the caching problem is NP-H ard . We develop effective lightweight heuristics for the latter. Empirical evaluation shows that H elix is not only able to handle a wide variety of use cases in one unified workflow but also much faster, providing run time reductions of up to 19x over state-of-the-art systems, such as DeepDive or KeystoneML, on four real-world applications in natural language processing, computer vision, social and natural sciences.