The rapid integration of Machine Learning (ML) in organizational practices has driven demand for substantial computational resources, incurring both high economic costs and environmental impact, particularly from energy consumption. This challenge is amplified in dynamic data environments, where ML models must be frequently retrained to adapt to evolving data patterns. To address this, more sustainable Machine Learning Operations (MLOps) pipelines are needed for reducing environmental impacts while maintaining model accuracy. In this paper, we propose a model reuse approach based on data similarity metrics, which allows organizations to leverage previously trained models where applicable. We introduce a tailored set of meta-features to characterize data windows, enabling efficient similarity assessment between historical and new data. The effectiveness of the proposed method is validated across multiple ML tasks using the cosine and Bray–Curtis distance functions, which evaluate both model reuse rates and the performance of reused models relative to newly trained alternatives. The results indicate that the proposed approach can reduce the frequency of model retraining by up to 70% to 90% while maintaining or even improving predictive performance, contributing to more resource-efficient and sustainable MLOps practices.

The rapid integration of Machine Learning (ML) in organizational practices has driven demand for substantial computational resources, incurring both high economic costs and environmental impact, particularly from energy consumption. This challenge is amplified in dynamic data environments, where ML models must be frequently retrained to adapt to evolving data patterns. To address this, more sustainable MLOps pipelines are needed that reduce environmental impacts while maintaining model accuracy. In this paper, we propose a model reuse approach based on data similarity metrics, allowing organizations to leverage previously trained models where applicable. We introduce a tailored set of meta-features to characterize data windows, enabling efficient similarity assessment between historical and new data. The effectiveness of the proposed method is validated across multiple ML tasks using cosine and Bray-Curtis distance functions, evaluating both model reuse rates and the performance of reused models relative to newly trained alternatives. The results indicate that the proposed approach can significantly reduce the frequency of model retraining while maintaining or even improving predictive performance, contributing to more resource-efficient and sustainable MLOps practices.

As ESG regulations gain relevance, namely following the entry into force of EU's Corporate Sustainability Reporting Directive, there is an increased requirement for value chain transparency. Organizations that are heavily based on data, Artificial Intelligence or Machine Learning, may significantly contribute to the ESG profiles of their upstream clients through the resources they spend on data storage and processing, model training or model serving. However, the lack of standardized mechanisms to report ESG-relevant indicators from these organizations hinders effective integration into their clients' ESG disclosures. This paper is motivated by an identified need to facilitate ESG indicator reporting by ML organizations in the value chain, and proposes an architecture to automate the monitoring and reporting process. This architecture is based on the principle of observability, and on the integration and effective use of open-source tools to monitor data processing pipelines and MLOps. By implementing an automated observability-based monitoring framework, ML organizations can provide actionable ESG data that align with regulatory requirements, while reducing reporting overhead for clients. We address how this architecture can be seamlessly integrated into existing operational workflows of ML providers and their clients, enhancing transparency, accountability, and compliance. The proposed architecture ensures accurate, real-time reporting and creates a scalable foundation for ESG aligned innovation in Machine Learning and adjacent domains.