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As machine learning becomes increasingly pervasive, its resource demands and financial implications escalate, necessitating energy and cost optimisations to meet stakeholder demands. Quality metrics for predictive machine learning models are abundant, but efficiency metrics remain rare. We propose a framework for efficiency metrics, that enables the comparison of distinct efficiency types. A quality-focused efficiency metric is introduced that considers resource consumption, computational effort, and runtime in addition to prediction quality. The metric has been successfully tested for usability, plausibility, and compensation for dataset size and host performance. This framework enables informed decisions to be made about the use and design of machine learning in an environmentally responsible and cost-effective manner.
Digital transformation strengthens the interconnection of companies in order to develop optimized and better customized, cross-company business models. These models require secure, reliable, and trace- able evidence and monitoring of contractually agreed information to gain trust between stakeholders. Blockchain technology using smart contracts allows the industry to establish trust and automate cross- company business processes without the risk of losing data control. A typical cross-company industry use case is equipment maintenance. Machine manufacturers and service providers offer maintenance for their machines and tools in order to achieve high availability at low costs. The aim of this chapter is to demonstrate how maintenance use cases are attempted by utilizing hyperledger fabric for building a chain of trust by hardened evidence logging of the maintenance process to achieve legal certainty. Contracts are digitized into smart contracts automating business that increase the security and mitigate the error-proneness of the business processes.