  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 post-processing 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 Helix, a declarative machine learning system that
accelerates iterative development by optimizing workflow execution end-to-end
and across iterations. Helix 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, Helix 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 Helix programming interface and the speed and ease of iterative
development using Helix. In our evaluations, Helix achieved up to an order of
magnitude reduction in cumulative run time compared to state-of-the-art machine
learning tools.
