论文标题
BPMN4SML：用于无服务器机器学习的BPMN扩展。机器学习工作流及其无服务器部署编排的技术独立和互操作建模
BPMN4sML: A BPMN Extension for Serverless Machine Learning. Technology Independent and Interoperable Modeling of Machine Learning Workflows and their Serverless Deployment Orchestration
论文作者
论文摘要
机器学习（ML）继续渗透到学术界，工业和社会的所有层面。尽管取得了成功，但缺乏以一致且连贯的方式捕获和代表机器学习工作流程的心理框架。例如，事实上的过程建模标准，业务流程模型和符号（BPMN），由对象管理组管理，被广泛接受和应用。但是，它没有具体的支持来表示机器学习工作流程。此外，用于部署机器学习解决方案的异质工具的数量很容易压倒从业者。需要进行研究以使从建模到部署ML工作流程的过程保持一致。 我们分析了针对机器学习工作流及其无服务器部署的基于标准的概念建模的要求。面对以技术独立且可互操作的方式对ML工作流的一致和相干建模的缺点，我们扩展了BPMN的元模型元设备（MOF）元模型和相应的符号，并引入了BPMN4SML（无服务器机器学习的BPMN）。我们的扩展BPMN4SML遵循BPMN对象管理组（OMG）引用的相同概述。我们通过提出概念映射将BPMN4SML模型转换为使用TOSCA的相应部署模型来进一步解决部署中的异质性。 BPMN4SML允许对整个机器学习生命周期中各种粒度和复杂性的机器学习工作流程与技术无关和互操作的建模。它有助于达到共享和标准化的语言来传达ML解决方案。此外，它采取了将ML Workflow模型图转换为通过TOSCA的无服务器部署的相应部署模型的第一步。
Machine learning (ML) continues to permeate all layers of academia, industry and society. Despite its successes, mental frameworks to capture and represent machine learning workflows in a consistent and coherent manner are lacking. For instance, the de facto process modeling standard, Business Process Model and Notation (BPMN), managed by the Object Management Group, is widely accepted and applied. However, it is short of specific support to represent machine learning workflows. Further, the number of heterogeneous tools for deployment of machine learning solutions can easily overwhelm practitioners. Research is needed to align the process from modeling to deploying ML workflows. We analyze requirements for standard based conceptual modeling for machine learning workflows and their serverless deployment. Confronting the shortcomings with respect to consistent and coherent modeling of ML workflows in a technology independent and interoperable manner, we extend BPMN's Meta-Object Facility (MOF) metamodel and the corresponding notation and introduce BPMN4sML (BPMN for serverless machine learning). Our extension BPMN4sML follows the same outline referenced by the Object Management Group (OMG) for BPMN. We further address the heterogeneity in deployment by proposing a conceptual mapping to convert BPMN4sML models to corresponding deployment models using TOSCA. BPMN4sML allows technology-independent and interoperable modeling of machine learning workflows of various granularity and complexity across the entire machine learning lifecycle. It aids in arriving at a shared and standardized language to communicate ML solutions. Moreover, it takes the first steps toward enabling conversion of ML workflow model diagrams to corresponding deployment models for serverless deployment via TOSCA.
