通过逻辑查询计划对原生机器学习流水线进行插桩与分析
Instrumentation and Analysis of Native ML Pipelines via Logical Query Plans
AI总结：
本文提出从机器学习流水线代码中提取逻辑查询计划，自动推断语义并进行插桩与重写，以支持数据溯源筛查和假设分析等用例，无需人工标注代码。
摘要：
机器学习（ML）越来越多地用于自动化具有重要影响的决策，这引发了对其正确性、可靠性和公平性的关注。我们设想构建高度自动化的软件平台，以协助数据科学家开发、验证、监控和分析他们的机器学习流水线。与现有工作不同，我们的核心思想是从依赖流行库的机器学习流水线代码中提取“逻辑查询计划”。基于这些计划，我们自动推断流水线语义，并对机器学习流水线进行插桩和重写，以支持多种用例，而无需数据科学家手动标注或重写代码。首先，我们开发了这样一种抽象的机器学习流水线表示，以及从 Python 代码中提取该表示的机制。其次，我们利用该表示高效地对静态机器学习流水线进行插桩，并应用数据溯源跟踪，从而实现对常见数据准备问题的轻量级筛查。最后，我们构建了自动重写机器学习流水线的机制，以执行更高级的假设分析（what-if analyses），并提出对由此产生的工作负载使用多查询优化。在未来的工作中，我们的目标是在数据科学家处理机器学习流水线时进行交互式辅助。
英文摘要：
Machine Learning (ML) is increasingly used to automate impactful decisions, which leads to concerns regarding their correctness, reliability, and fairness. We envision highly-automated software platforms to assist data scientists with developing, validating, monitoring, and analysing their ML pipelines. In contrast to existing work, our key idea is to extract "logical query plans" from ML pipeline code relying on popular libraries. Based on these plans, we automatically infer pipeline semantics and instrument and rewrite the ML pipelines to enable diverse use cases without requiring data scientists to manually annotate or rewrite their code. First, we developed such an abstract ML pipeline representation together with machinery to extract it from Python code. Next, we used this representation to efficiently instrument static ML pipelines and apply provenance tracking, which enables lightweight screening for common data preparation issues. Finally, we built machinery to automatically rewrite ML pipelines to perform more advanced what-if analyses and proposed using multi-query optimisation for the resulting workloads. In future work, we aim to interactively assist data scientists as they work on their ML pipelines.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
