Improving Machine Learning Pipeline Creation using Visual Programming and Static Analysis

Abstract

ML pipelines are composed of several steps that load data, clean it, process it, apply learning algorithms and produce either reports or deploy inference systems into production. In real-world scenarios, pipelines can take days, weeks, or months to train with large quantities of data. Unfortunately, current tools to design and orchestrate ML pipelines are oblivious to the semantics of each step, allowing developers to easily introduce errors when connecting two components that might not work together, either syntactically or semantically. Data scientists and engineers often find these bugs during or after the lengthy execution, which decreases their productivity. We propose a Visual Programming Language (VPL) enriched with semantic constraints regarding the behavior of each component and a verification methodology that verifies entire pipelines to detect common ML bugs that existing visual and textual programming languages do not. We evaluate this methodology on a set of six bugs taken from a data science company focused on preventing financial fraud on big data. We were able detect these data engineering and data balancing bugs, as well as detect unnecessary computation in the pipelines.