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Machine learning (ML) continues to permeate all layers of academia, industryand society. Despite its successes, mental frameworks to capture and representmachine learning workflows in a consistent and coherent manner are lacking. Forinstance, the de facto process modeling standard, Business Process Model andNotation (BPMN), managed by the Object Management Group, is widely accepted andapplied. However, it is short of specific support to represent machine learningworkflows. Further, the number of heterogeneous tools for deployment of machinelearning solutions can easily overwhelm practitioners. Research is needed toalign the process from modeling to deploying ML workflows.  We analyze requirements for standard based conceptual modeling for machinelearning workflows and their serverless deployment. Confronting theshortcomings with respect to consistent and coherent modeling of ML workflowsin a technology independent and interoperable manner, we extend BPMN'sMeta-Object Facility (MOF) metamodel and the corresponding notation andintroduce BPMN4sML (BPMN for serverless machine learning). Our extensionBPMN4sML follows the same outline referenced by the Object Management Group(OMG) for BPMN. We further address the heterogeneity in deployment by proposinga conceptual mapping to convert BPMN4sML models to corresponding deploymentmodels using TOSCA.  BPMN4sML allows technology-independent and interoperable modeling of machinelearning workflows of various granularity and complexity across the entiremachine learning lifecycle. It aids in arriving at a shared and standardizedlanguage to communicate ML solutions. Moreover, it takes the first steps towardenabling conversion of ML workflow model diagrams to corresponding deploymentmodels for serverless deployment via TOSCA.

BPMN4sML: A BPMN Extension for Serverless Machine Learning. Technology  Independent and Interoperable Modeling of Machine Learning Workflows and  their Serverless Deployment Orchestration