What is EDSJ? The European Data Science Journal (EDSJ) is an open-access, peer-reviewed international journal dedicated to publishing high-quality, original research in Data Science, Analytics, and data-driven innovation. Published continuously with rapid turnaround, EDSJ is indexed in Scopus (Elsevier), affirming its adherence to the highest global standards of peer review, editorial quality, and research integrity. EDSJ provides a rigorous academic platform for researchers, scholars, and industry professionals to disseminate innovative ideas, theoretical developments, applied methodologies, and cutting-edge advancements across the rapidly evolving landscape of data science. The journal welcomes both foundational and applied contributions with interdisciplinary reach spanning healthcare, finance, engineering, social sciences, cybersecurity, and sustainability.
Medallion-Based Feature Engineering for Real-Time MLOps
As large-scale machine learning in production becomes ubiquitous across various industries, both engineering and research-oriented teams are pushing the limits for faster and continuously retrained models. Nevertheless, building and running a model, even using a well-established MLOps framework, is often only a small part of the overall task. Feature engineering and feature selection typically consume the majority of both the data and ML time of a project. A Medallion Architecture specifically designed to assist in real-time feature engineering, along with the establishment of an enterprise Feature Store capability, can help to speed this process, integrating the latest insights from the data into models and putting both new feature and model within continuous integration pipelines. The architecture incorporates key DataOps and MLOps best practices for target labeling, feature quality, and CI/CD pipelines, while enabling features to be built in parallel either as part of a dedicated MLOps modeling step or part of the standard product development process. ML pipelines and Feature Stores for Scalable Real-Time Feature Engineering cover Medallion Architecture and a large-scale Medallion Implementation, integrating best practices for Real-Time Streaming Data Ingestion and Processing as well as Continuous Integration and Continuous Delivery for Features. As a result, a Medallion Architecture designed for data scale considers the needs of engineering-focused feature engineering while incorporating horizontal scaling through sharding strategies aligned with both features to be built and cost objectives around exploratory data and feature selection efforts.
Copyright (c) 2024 European Data Science Journal (EDSJ)
AI-Powered FHIR Framework for Real-Time Health Data Exchange
The Fast Healthcare Interoperability Resources (FHIR) specification defines a set of API-based interoperability resources, established by Healtcare Information Technology; it is rapidly becoming the standard for the exchange of clinical data in the global healthcare space. The proposed framework's integration into all healthcare systems would allow bi-directional real-time conversion between any clinical data and FHIR representation. The bi-directional cross-domain codes are FHIR-based SNOMED CT and ICD-10-CM for the UK and ICS-10-CA for Canada. These general-purpose codes are standardised in LOINC for procedures, diagnostic tests, investigations, and COVID-19. LOINC coding is performed by a hybrid human-AI approach, combining with prompt engineering and meta-vision a generally-used machine-learning model with a new detection of complex LOINC code graphs. The generated LOINC code is integrated with the general interchangeable SNOMED CT. The framework considers as input any unstructured clinical data and provides as output a set of FHIR-compliant resources with complete ICD-10-CM association. Generative AI is currently the most important priority of research and development in the IT space, including the healthcare sector. ChatGPT is a breakthrough application, and its rapid success is stimulating and motivating a cascade of new similar generation AI products and developments. The attention, the financial commitment, and the growth rate of one product after the other clearly demonstrate the interest of generative AI to the business world, to the economy of the software industry, and to society as a whole worldwide. Healthy Data's healthcare management system successfully harnessed the benefits of generation AI for FHIR-compliant coding of data exchange, and thus broad preparations, in terms of technology and content, are being undertaken for the next baby step towards a full generative AI product for healthcare.
Precision Health Analytics through Intelligent EHR Engineering
Healthcare systems generate large volumes of Electronic Healthcare Record (EHR) data. To utilize the information for clinical intelligence that facilitates decision making in clinical routine, AI-driven systems are developed, built upon sound Clinical Data Engineering principles. Three AI-driven systems are presented: 1) Clinical Data Engineering with Application Programming Interfaces for hospitals using the Seven Interoperable Levels of Data Standardization. API-based data-sharing services enable clinical data normalization for secondary use and enrich suitability for clinical analytics. 2) High-quality risk prediction and patient stratification in patients undergoing elective cardiac surgery through integration of log data from an Electronic Healthcare Record Clinical Decision Support System in combination with information supplied by the EHR. The performance obtained applying the gradient-boosted tree algorithm set a benchmark for these objectives. 3) An AI-driven system for feature engineering covering the clinical information standardization for EHR analytics and highlighting attributes related to patient outcome. Data preparation and feature extraction pipelines automatically assemble the complete clinical dataset by extracting and transforming information directly from the database while providing data for unsupervised feature selection. Effective EHR analytics constitutes a prerequisite for the success of precision healthcare decision systems. Validated stratification and risk-prediction solutions constitute fundamental building blocks within a precision healthcare concept. Correspondingly, the strategies proposed for stratifying patients undergoing elective cardiac surgery and forecasting their outcome set the path for further developments, enriching the integration of analysis log data from an Electronic Healthcare Record Clinical Decision Support System with the information available in the EHR.
Copyright (c) 2025 European Data Science Journal (EDSJ)
Interpretable Sanctions Screening in Cloud-Native Financial Systems
Cloud-native technologies enable the optimal use of cloud providers’ services, offering data storage, processing, and compute capabilities that automatically scale according to demand. Government regulations imposed on financial institutions require real-time sanctions screening of customers, transactions, and parties involved in transactions. Screening is often supported by third-party services that read large amounts of data into proprietary databases. Customers and transactions are repeatedly screened against the same lists, producing millions of false positive alerts that require human intervention. Many of these alerts are spurious and non-suspicious. Increasing the efficiency of regulatory workflows using explainable artificial intelligence (AI) is therefore a priority for cloud-native financial institutions. An AI framework uses machine-learning models that allow interpretations and explanations to satisfy anti-money-laundering specialists. Explainability enhances the quality of the detection model, and the explanations allow compliance, risk, and audit functions to better understand the risk assessment. Training using data generative techniques ensures availability of sufficient samples for the detection scenarios, mitigating the industry challenge of imbalanced datasets with too few samples for model training. Deep neural approaches, supported by embedding techniques that offer a simplified representation of high-dimensional custom datasets, further improve performance.
Intelligent Data Pipelines for Next-Generation Manufacturing Quality Assurance
AI-Powered Data Engineering Frameworks for Smart Manufacturing Quality Control presents an evidence-based, formal analysis of AI methods, data pipelines, and governance to improve defect detection and process reliability in smart manufacturing quality control. The contributions cover data engineering prerequisites—including data sources, quality requirements, acquisition approaches, ingestion methods, latency considerations, and integration—together with key decision-supporting AI techniques, a comprehensive system architecture for end-to-end quality control, and high-level data governance requirements. Exploiting artificial intelligence (AI) to enhance manufacturer-automated quality control processes enables self-driving factories with reduced defect rates. AI methods are implemented for defect detection, correlation, and root-cause forecasting, closing the gaps between Machine Learning, Big Data, and IoT. Data quality proves decisive for these operations, raising specialized Data Engineering requirements across the entire analytical pipeline and including Quality Control Data Engineering-as-a-Service. By framing the analysis within the broader context of smart factory data engineering, a comprehensive set of Quality Control data quality requirements emerges and combinations of supervised, unsupervised, and time series methods are explored to tackle both defect detection and repair procedure prediction.
Dimitris Plexousakis Dimitris Plexousakis
Designing Multi-Agent Ecosystems for Intelligent Customer Experience Automation
A multi-agent approach supports more complex, realistic, and timely interaction scenarios for both customers and companies. Instead of developing monolithic dialogue systems that can support requests from any customer, multi-agent systems permit dedicated agents for specific tasks, offering richer content and handling more intricate interactions. Dialogue systems based on either hierarchical or federated multi-agent architectures are reviewed, and these two patterns are explored as foundations for customer experience management platforms in which multiple agents can automate a variety of customer interaction scenarios. A multi-agent approach supports more complex, realistic, and timely interaction scenarios for both customers and companies. Instead of developing monolithic dialogue systems that can support requests from any customer, multi-agent systems permit dedicated agents for specific tasks, offering richer content and handling more intricate interactions. Dialogue systems based on either hierarchical or federated multi-agent architectures are reviewed, and these two patterns are explored as foundations for customer experience management platforms in which multiple agents can automate a variety of customer interaction scenarios.
Predictive Intelligence for Sustainable Urban Energy Management
The challenge of satisfying ever-growing energy demand while respecting environmental constraints is critical for both human history and present circumstances. Indeed, energy systems have gradually transformed, becoming remarkably complex over the last century, in order to meet the developments of the associated end-use sectors and continue to provide energy services. In this context, the management and operation of the energy systems at different timescales represent increasingly difficult tasks — especially for large geographical areas — but are fundamental to ensuring that daily lives are not unduly affected by energy shortages. Demand-side management and demand response have appeared on the energy scene and become increasingly relevant over the years in the quest for sustainable development. Indeed, energy efficiency improvements and the optimization of the energy use at the demand level have great potential for operating and managing service supply in a sustainable way. Although demand-side management, demand response, and energy efficiency have very different goals, characteristics, and symbolism, they share many common traits that have led to their convergence and the merging together of demand-side activities. For this reason, such activities can be managed from a common framework in order to provide effective artificial intelligence–enabled predictive analytics in support of sustainable energy consumption also for smart (or intelligent) cities.
Resilient Ingestion for High-Volume Healthcare Data
Healthcare organisations capture an unprecedented volume of transactions as the industry commits to science-based practices. As with traditional large-scale transactional systems, ensuring the reliability and correctness of these transactions is imperative. However, the need for fast ingestion creates unique challenges for the underlying data-integration pipelines. Conventionally, data ingestion achieves high throughput through complex ETL jobs that execute on long-running schedules, leading to long processing latency. Yet these ingestion jobs often operate in isolation from the upstream applications that produce the data. Containing data quality issues—ensuring data accuracy, security, and compliance with regulations such as HIPAA or GDPR—requires significant additional engineering effort, creates additional costs, and often introduces delays or failure to analyse or respond to incidents. With these factors in mind, the teams operating data systems at a healthcare organisation targeting the design of clinical systems, patient-facing products, and data-integration functions invested in the design and implementation of ingestion components that actively make use of the data as it is being ingested. To support the fast-paced nature of modern clinical environments—such as emergency rooms, tranfsers between care facilities, and life-supporting interventions—the Data Engineering Team started with the premise of making all data-integration processes as real-time as possible. A key area of focus was the reliability of data ingestion, viewed primarily as an extension of preventative maintenance within production environments. With operational integrity and data quality as core themes, the appropriate architectural foundations ensuring ingestion reliability were established and best-practice reliability mechanisms proposed. Ingestion can thus proceed reliably when the appropriate principles are applied, proven patterns are implemented, and the appropriate controls are in place; but achieving high availability and quality data requires purposeful and committed effort.
AI Risk and Compliance in Global Infrastructure
Ethics-driven governance of AI-enabled global data center infrastructure is pivotal for protecting security and privacy interests across borders. A multilayered compliance architecture—including risk assessment methodologies, technical mechanisms, established roles, and a set of policy instruments—supports ethical risk and sovereign compliance across the data center ecosystem. Ethical risk management comprises privacy-preserving data flows, lifecycle security, and AI governance. Because ethical risk spans the global ecosystem, fostering collaboration and commonality builds trust and reduces burden while buttressing legitimacy. Thus, private and public interests align. Sovereign AI governance is an essential enabler of the regionally sovereign world of such infrastructure. AI is reshaping the global digital landscape, including the infrastructure of its cloud and edge components. At the same time, the reliance on such AI-systems and the attendant cross-border exchange of data are raising concerns—notably, for security, privacy, democracy, and human rights. Addressing these ethical issues is a pressing requirement. However, the prevailing compliance frameworks for cross-border data flows and AI systems provide neither the tools nor the appropriate level of granularity for Ethical Risk Management in this context. A distinctive multilayered Compliance Architecture is thus put forward that is adapted specifically to the Ethical Risks associated with AI-enabled Global Data Center Infrastructure, namely, that of Operational Resilience, Legal Certainty, and the protection of Security, Privacy & Human Rights.
EXTREME GRADIENT BOOSTING -DRIVEN HYDROLOGICAL FORECASTING STREAMFLOWUNDER CMIP6 SCENARİOS
Accurate streamflow forecasting is essential for sustainable water resource planning under increasing climate variability. This study investigates the performance of the Extreme Gradient Boosting (XGBoost) algorithm for monthly streamflow prediction in the Ceyhan River Basin, utilizing climate projections from General Circulation Models (GCMs) participating in CMIP6. The model was tested across four Shared Socioeconomic Pathways (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5), using historical discharge data and climate predictors such as mean sea level pressure (mslp) and specific humidity (shum) as inputs. Evaluation metrics included RMSE, MAE, and the Nash–Sutcliffe Efficiency (NSE). The results show that the XGBoost model dynamically adapts to seasonal patterns and selects the best-performing configuration each month. Under high-emission scenarios, particularly SSP5-8.5, streamflow variability significantly increased. SHAP (SHapley Additive exPlanations) analysis enhanced model interpretability, identifying shum and mslp as key predictive features. These findings highlight the potential of XGBoost as a robust and explainable decision-support tool for climate-informed hydrological modeling.
Prof.Dr., Aytaç GÜVEN Prof.Dr., Aytaç GÜVEN
Cloud-Native Data Ecosystems for Predictive Industrial Decisions
Industrial organizations are rapidly adopting cloud technologies to reduce infrastructure management costs and increase operational flexibility. Nonetheless, existing cloud deployment uses are mainly focused on infrastructure as a service and data storage. In parallel, the demand for data-driven predictive modeling has grown and continues to evolve beyond standard business intelligence. As a result, cloud-native software development and deployment models have attracted significant attention from academia and industry. However, related research into the cloud-native paradigm remains limited and tends to offer design and development support for a single application or service without addressing the complete data ecosystem. The data processing and predictive modeling lifecycle requires a cloud-native architecture that supports large-scale predictive modeling operations and the construction of a data-oriented infrastructure. Building intelligent capabilities involves orchestrating all machine learning operations in the cloud, integrating advanced machine learning lifecycle frameworks, incorporating feature stores, and enabling real-time scoring orchestration and execution algorithms. A predictive decision-making data ecosystem focuses on the complete data processing pipeline in relation to predictive models, integrating model data processing and inference pipelines into orchestration and scheduling frameworks while supporting event-driven workflows.
Copyright (c) 2023 European Data Science Journal (EDSJ)
An integrated framework for secure transformation and operational optimization enables enterprises to harness Generative Artificial Intelligence (GAI) alongside autonomous systems while managing security and regulatory compliance. Key development goals include limiting risk exposure incurred by data provision, model utilization, and decision-making across generative AI applications; promoting security by design in language model deployment; aligning enterprise policies with relevant laws, industry standards, and certification requirements; ensuring comprehensive auditability of data, models, and decisions; and formally incorporating GAI capabilities within autonomous decision systems. The approach is evaluated through evidence from two application scenarios. Capabilities for language-led operations, integrating all requisite data, and completing digital tasks while meeting security requirements are confirmed. Security-aware autonomous decisions seamlessly integrated within enterprise risk management frameworks are also demonstrated. Close examination of the governing integration architecture substantiates the framework’s contribution and adaptability to other areas requiring intelligent enterprise transformation. The recent emergence of GAI in a variety of enterprise applications presents significant opportunities for operational excellence. At the same time, a distinct shift in the profile of enterprise GAI applications since late 2022 imposes urgent new security requirements that must be addressed through appropriate risk assessments, threat modeling, and security-by-design deployment of language models. Enterprises are now increasingly exposed to advanced persistent threats and malicious actors with novel capabilities. Data may need to be supplied to language models to meet specific objectives, such as translating proprietary information or language-driven operational excellence. In this context, the role of GAI capabilities in any autonomous decision system must be well defined and the associated risks minimized to facilitate safe exploitation.
Copyright (c) 2026 European Data Science Journal (EDSJ)
Generative AI for Rare Disease Data Augmentation
Shallow Learning agents require abundant labelled data to excel. In rare diseases, where patient population is often limited to a few hundred patients, such a data source is not readily available. Generative AI models can synthesize new, similar data by learning an approximate distribution of the original data. Their application in rare disease cohorts and clinical data is detailed, with supervisory signals derived from the data labelling mechanisms. The response from Generative AI models is further validated by comparing model predictions with real-world outcomes. Rare diseases are conditions that affect a small percentage of the population — definitions suggest a prevalence of below 1:2000 but this varies among nations. Globally, a collection of more than 7000 rare diseases, affecting 25 million patients, has been enumerated, with the rapid emergence of new entities identified. Early diagnosis, clinical management, and developing novel therapeutics for rare diseases offer challenging problems with limited labelled data-sets available to Deep Learning agents. Synthetic Data Generation, by augmentation or providing an alternative data source, enables Shallow Learning agents to attempt these tasks across phenotype, genotype, and narrative data.
Big Data Foundation Models for Personalized Medicine
Deep learning models pretrained on multimodal Big Data may be used to address many problems in Precision Medicine. Choi et al. (2022) provided background on the core model architectures and training paradigms; on the prevalent types of multimodal medical data; and on the preliminary works aimed at combining model frameworks and training paradigm across existing modalities. Multimodal Foundation Models are introduced on the basis of learned medical representation and multimodal representation transfer and design. The explored areas are centered on data ecosystems that can support the future development of Deep Learning within Precision Medicine using multimodal Big Data. As subtitled, the section on Data Ecosystems and Governance focuses on data acquisition, curation and quality assurance, privacy, security and ethical considerations. Applications involve the integration of genomics and transcriptomics; the use of medical image and radiomic data; and the building–testing of benchmark datasets to alleviate data bias. The addressed methodological challenges discuss data bias, fairness and generalizability; interpretability and clinician trust; benchmarking protocols; reproducibility; and open science practices.
Deep Neural Networks for Clinical Imaging Diagnosis
Recent advances in deep learning have generated excitement within the domain of medical imaging diagnostics. Promising results achieved in specific tasks, often with limited training data, have led to calls for scaled-up applications leveraging large-scale medical imaging datasets. Such an approach could facilitate rapid and cost-effective development of diagnostic algorithms based on different imaging modalities. Nevertheless, a crucial step toward subsequent clinical uptake of these methods is the successful deployment of a large, multicenter test set for performance evaluation. However, despite being essential to the transfer of deep learning methods into clinical practice, scalable evaluation and deployment of these technologies have yet to receive significant attention. The attractions of large-scale testing and the potential effects of scale on accuracy are therefore often overlooked. Analysis of deep learning methods — including convolutional neural networks, transformers, and other architectures — capable of utilizing large-scale clinical imaging datasets to achieve comparable or superior sensitivity, specificity, and area under the curve values to human clinicians is presented. Key strengths of the methodology include the emphasis on large-scale datasets, consideration of diagnostic performance across diverse clinical specialties, and application of external validation datasets to assess generalization performance across multiple sites. These qualities make the method particularly suitable for the evaluation of potentially biased or miscalibrated systems.
Machine Learning for Clinical Risk Prediction in Big Data
Healthcare is a path-breaking field for big data. By combining electronic medical record data with omics data (genomics, proteomics, metabolomics, etc.), lifestyle information (e.g., smoking, drinking, diet, and exercise), social determinants of health, and relevant data from wearable devices, a diverse array of clinical and biological predictive models can be constructed. In particular, the application of machine-learning (ML) methods for clinical risk-prediction modeling has gained impressive momentum in recent years, amassing a wealth of reference literature. Unlike traditional statistical approaches commonly utilized in clinical applications, ML techniques have the potential to simultaneously leverage high-dimensional, heterogeneous data. This contribution reviews multiple important aspects of risk prediction using big data and ML methods, including data-sources, framework, performance metrics, and regulation. Relevant clinical applications span almost every area, including cardiovascular medicine, oncology, infectious diseases, nephrology, rheumatology, and psychiatry. Although numerous ML-based risk-scoring systems with impressive performance are found in the literature, external validation and transportability remain critical challenges that merit further exploration.
Foundations of High-Volume Clinical Data Architecture
IArchitectural frameworks for large-scale Electronic Health Record (EHR) data platforms are described. Existing EHR data platform architectures often leverage multiple cloud-based solutions blended with institutional infrastructures to manage and analyze clinical data at scale. Key design principles governing the scale of existing EHR data architecture include model design, governance structure, data access management, data security/policy/protection, data-information-language-based standardization, and analytics tool alignment, among others. The rapidly evolving technology landscape and the unprecedented volume of incident and retrospective clinical data being collected and generated within healthcare organizations have led to the emergent need for a dedicated architectural framework to support large-scale computing in the health informatics domain. The application areas of large-scale computing in health informatics include real-time predictive analytics, risk stratification, patient cohort analytics, development of predictive models for specific institutions or population groups, and many more. The use of EHR data for a multitude of decision-making processes in both clinical and non-clinical settings has prompted the establishment of policies prescribing the conditions of access and use of EHR data for non-employed individuals in the organization. Consequently, the demand for accessing, using, and managing EHR data at scale has impacted the overa
Responsible and secure governance frameworks are crucial for the use of Generative AI (GenAI) agents in domains rich with data from multiple parties, where compliance with ethical standards and legal requirements is mandatory. The proposed GenAI frameworks support the implementation of the principles of data provenance and data lineage, enabling fine-grained access control and consent management during the complete life cycle of each data item, from creation and storage by original data subjects or their representatives through use by third parties and deletion. An additional set of requirements establishes conditions for effective risk assessment and mitigation of negative consequences associated with the GenAI agents’ operations. Transparency and accountability of the agents’ operations and generated outputs must also be ensured, with the ability to preserve a detailed log of all actions, including data provenance and lineage metadata. A modular and extensible architectural pattern allows the implementation of these governance features as plug-in components detached from the core functionalities of the agents. Such decoupling supports the easy design and adoption of governance controls addressing the specific compliance, security, safety, and ethical considerations required for different deployment contexts. Further support for responsible operation relies on resource-aware model management capabilities that monitor and characterize the demand for compute, memory, and energy resources by the agents’ underlying models and make budget allocation and throttling decisions.
A Machine Learning and Big Data Approach
Agriculture produces the food, fiber, and feed essential for humanity even though it encompasses merely 4% of the Earth’s land surface. Nevertheless, through the consumption of water and energy, greenhouse gas emissions, land-use changes, and direct and indirect effects on biodiversity, agriculture plays a major role in the depletion of natural resources and environmental deterioration. In the long run, climate and ecological changes may also have adverse effects on agricultural production, thus threatening food security and consequently human well-being. In this context, guaranteeing and securing wise and judicious natural resource consumption is a key issue and challenge for realizing sustainable development. These opposing trends have spurred research into eco-efficient agriculture, that is, maximizing agricultural output (crops, animal protein, etc.) using the least possible amount of natural resources. They have also motivated the demand for tools capable of monitoring the state of agricultural ecosystems and enabling forecasting and decision support. The aim could be to assess and promote a more suitable input–output equilibrium and adopt agricultural practices compatible with society’s ecological sustainability objectives. Such tools rely on data ecosystems for continuous monitoring, Big Data architectures for data collection, and smart data pipelines for enabling quick real-time decision support or for batch production using advanced Machine Learning capabilities.
Event-Driven AI for Real-Time Sanctions Screening
A crucial task in financial crime compliance is sanctions screening, which involves determining whether organizations or individuals should be restricted from engaging in business relationships with a financial institution or the institution’s customers. The evolving geopolitical landscape has led to increasingly extensive and dynamic sanctions lists. Regulatory scrutiny on the adequacy of screening controls has heightened, with realtime detection of potential matches emerging as the next frontier for more forward-thinking institutions. A monitoring-based industry guidance paper jointly published by the Basel Committee and Financial Action Task Force calls for greater use of artificial intelligence in sanctions compliance. Despite this opportunity, a current shortcoming is that matching against sanctions lists continues to be predominantly based on batch processing, involving either screening transactions against sanctions list prior to pass through the bank, or screening customers’ information against sanctions list prior to onboarding or at defined refresh frequency. Suitable event-driven architectural patterns and cloud-native platforms can help financial institutions close this gap.
Carbon-Aware Strategies for Predictive Analytics on AWS and GCP
Achieving energy-efficient cloud-based predictive-analytics deployments with reduced carbon emissions presents an important challenge. Existing approaches focus on optimizing performance and operational costs without considering their long-term impact on the growing carbon footprint. Moreover, green deployments are rarely integrated into decision making. A carbon-aware model-deployment strategy is presented, which incorporates carbon-intensity forecasts to minimize emissions. Carbon-aware scheduling and spatial routing balance latency and grid carbon-intensity while enabling real-time predictions in hybrid edge-cloud deployments. The objective functions and constraints are flexible, supporting trajectory optimization, temporal alignment with renewable-energy-generation patterns, and energy-awareness during model training. Analyses of the Google Cloud Platform and Amazon Web Services elucidate how services and tools can be leveraged to execute carbon-aware deployments. The approach is demonstrated in three carbon-aware case studies: real-time inference, batch training with regional carbon-minimization, and AutoML hyperparameter tuning. The proposed formulations provide decision-support information for directing green predictive-analytics workloads across cloud infrastructure. Cloud-based predictive-analytics workloads typically follow a life cycle that encompasses model training, hyperparameter tuning, modeling, and inference. The infrastructures provided by cloud-service operators make it possible to elastically allocate these resources according to demand, achieving higher-performance and lower-cost deployments. Some service products allow inference to be geographically and temporally aligned with the request origin, reducing latency and improving user experience. Nonetheless, these predictions are rarely planned on the basis of grid carbon-intensity forecasts to achieve a smaller environmental impact—and carbon footprint—during the exploitation phase.
Green AI for Financial Derivatives and Cloud Compliance
Enterprises are responding to climate- and sustainability-oriented mandates through Green Artificial Intelligence (AI), a concept encompassing energy-efficient and carbon-aware approaches to AI development and deployment. A foundational understanding of Green AI is established, along with the supporting concepts of Energy and Carbon-Aware, Sustainable Computing, and Lifecycle Assessment. These construct a framework for applying Green AI to the orchestration of enterprise-scale financial functions, spanning efficiency-enhancement across derivatives pricing, hedging, and risk processes; automation and energy-awareness in regulatory compliance; and holistically defined Cloud Compute Efficiency metrics. Emerging drivers solidify Green AI as a business imperative. Cost reduction remains a central goal; underlying initiatives for resource optimization have broadened in scope, framing Green AI as a tool for climate-risk remediation and a pathway for satisfying evolving regulatory requirements both in capital markets and more widely within enterprise Governance, Risk and Compliance processes. Artefact preservation for internal and external audit purposes places traceability demands across a range of decision processes; the need for energy-usage disclosures drives automation of compliance with established guidelines. Societal pressures catalyse both hard regulation and voluntary endorsement of targets, goals, and principles that require quantifiable response.
AI for Sustainable Automotive Lifecycle Optimization
Analysing sustainability at all levels of the automotive manufacturing lifecycle requires AI methods that can fuse data from multiple sources. Considerable progress is being made in planning, scheduling, control, and maintenance. With the right data acquisition and governance, the environmental, economic, and social performance of manufacturing companies can be optimised concurrently. Ultimately, those companies that integrate these capability improvements in a seamless, intuitive way stand to gain the most. AI systems can help achieve this—provided they are implemented wisely, with sufficient understanding of limitations. Considered action can promote quicker adoption of manufacturing AI and a more sustainable sector. Attention must focus on the right problems and solutions, with key emerging technologies applied effectively to positive effect. A detailed analysis of the complex interactions shaping automotive manufacturing enables better decisions in multiple areas—including resource use, EMissions Control, quality, safety, and total cost. AI methods form a powerful toolbox for addressing such concerns. However, there is ultimately no universal solution. Instead, the appropriate selection, development, and integration of solutions is vital. Consequently, action is required to widen the scope of AI applications beyond the obvious use cases of predictive maintenance and real-time anomaly detection, making it possible to identify and analyse emerging, less obvious problems.