Machine Learning Platform Revenue Recognition: Computational Optimization of IFRS 15 Implementation in Cloud-Based AI Service Architectures

Abstract

This paper develops computational methods for optimizing revenue recognition in machine learning platforms operating on cloud computing infrastructure. We analyze how Artificial Intelligence as a Service (AIaaS) platforms leverage distributed computing architectures, containerization technologies (Docker, Kubernetes), and microservices patterns to deliver AI capabilities, creating complex revenue recognition challenges under IFRS 15. Our research employs algorithmic analysis to examine five critical technical challenges: (1) computational resource allocation tracking across multi-tenant cloud environments, (2) real-time transaction price determination using usage metering APIs and consumption-based billing algorithms, (3) automated revenue allocation across platform components using distributed ledger technologies, (4) temporal revenue recognition optimization through event-driven architectures and streaming data processing, and (5) network effect quantification using graph algorithms and data analytics.