Facilitating Interdisciplinary Teams to Build Better AI-Based Systems

智能软件系统帮助我们生活的各个领域，如电子商务网站、社交媒体和网络搜索。当使用人工智能组件构建智能软件系统时，由(但不限于)数据科学家和软件工程师组成的跨学科团队需要合作。这些团队有不同的目标和经验，往往会导致过程中的摩擦：数据科学家主要在数据探索阶段工作，训练高性能的机器学习模型，这是一个高度探索性和迭代的过程。然后，他们将生成的学习代码和模型交付给软件工程师，以便在生产阶段将模型集成到生产代码中。有证据表明，通常需要重构探索阶段的代码，以适应生产方面的问题，如延迟、可伸缩性和健壮性。此外，一旦开发和生产模型逐渐分离，整合来自生产阶段的反馈以进行额外的试验并不是一件容易的事，这通常需要大量的协调。这会引入错误，减慢开发过程，并增加对协调开销的需求。目前，大型科技公司和小型初创团队都在努力将机器学习的想法转化为可以无缝集成到软件系统中的人工智能组件。该研究项目的主要目标是促进数据科学家和软件工程师之间的合作，减少摩擦，并为基于AI的软件开发生命周期提供支持。特别是，我们的目标是实现三个目标：(1)确定特定于上下文的协作痛点和最佳实践；(2)在数据探索阶段改进代码质量和编码环境；以及(3)促进数据科学家和软件工程师之间的协作。主要成果包括设计和开发支持协作和系统建设的分析基础设施和干预措施。该研究计划将通过大规模软件分析的实践研究实践，培训10名HQP(2名博士、3名硕士、5名USR)。研究活动涉及对软件从业者和AI专家的深入用户研究，对问题空间的实证调查，以及对解决问题的方法的严格设计和评估。此外，所有HQP将获得软件工程、机器学习和软件开发方面的技能和知识，并将有机会在真正的、高度有效的软件系统上工作，并建立强大的动手技能。考虑到广泛的应用场景，我们的研究成果可用于支持来自不同背景的专注于机器学习的团队的协作和系统建设，包括安大略省、加拿大和国际上的老牌公司、初创企业、非科技公司、非营利性组织和研究机构。

Intelligent software systems assist in every area of our lives, such as e-commerce sites, social media, and web searching. When building intelligent software systems with AI components, interdisciplinary teams consisting of (but not limited to) data scientists and software engineers need to work together. These groups have different goals and experience, often leading to friction in the process: Data scientists mainly work in the data exploratory phase to train a high-performing machine learning model, a heavily exploratory and iterative process. Then they deliver the resulting learning code and models to software engineers in order to integrate the model into production code in the production phase. Evidence shows that it is common that the code from the exploratory phase often needs to be refactored in order to accommodate production concerns, such as latency, scalability, and robustness. Additionally, once development and production models drift apart, it is non-trivial to incorporate feedback from the production phase for additional experimentation, often requiring significant coordination. This introduces mistakes, slows down the development process, and increases the need for coordination overhead. Currently, both big tech corporations and small start-up teams struggle with transitioning machine learning ideas into AI components that can be integrated into the software system seamlessly. The main goal of this research program is to foster collaboration and reduce friction between data scientists and software engineers, and provide support to the AI-based software development lifecycle. In particular, we aim to achieve three objectives: (1) Identifying context-specific collaboration pain points and best practices; (2) improving code quality and coding environment in the data exploration phase; and (3) facilitating collaboration between data scientists and software engineers. The main outcome includes the design and development of analysis infrastructure and interventions that support collaboration and system building. This research program will train 10 HQP (2 Ph.D., 3 MASc, 5 USR) through hands-on research practices in large-scale software analyses. The research activities involve in-depth user studies of software practitioners and AI experts, empirical investigation of the problem space, and rigorous design and evaluation of the methods to solve the problem. In addition, all HQP will gain skills and knowledge in software engineering, machine learning, and software development, and will have the chance to work on real, highly impactful software systems and build strong hands-on skills. Given the wide range of application scenarios, our research results can be applied to support collaboration and system building for teams focused on machine learning from different backgrounds, including established companies, start-ups, non-tech corporations, nonprofit, and research institutions in Ontario, in Canada, and internationally.