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Phishing detection models degrade quickly due to drift, adversarial evasion, and fairness issues. Existing MLOps platforms mainly automate deployment and monitoring. Prior works have examined SHAP-based monitoring, retraining, or fairness audits separately, but lack an integrated theory of resilience for adversarial environments. We introduce the Hybrid MLOps Framework (HAMF), a system designed to embed resilience and ethical governance into the lifecycle of phishing detection models. HAMF is ‘hybrid’ because it unifies proactive and reactive adaptation, combining automation with stakeholder oversight, and embedding resilience with ethical governance. HAMF treats resilience as an integrated lifecycle property, designed to simultaneously preserve model accuracy, fairness, and stakeholder trust amidst concept drift. Methodologically, HAMF implements this through a hybrid control cycle. This cycle fuses four key capabilities: SHAP-guided feature replacement, event-driven retraining, fairness-triggered audits, and structured human feedback. Unlike conventional pipelines where these functions are isolated, HAMF ensures their interdependence as first-class triggers. Empirical evaluations on large-scale phishing streams demonstrate HAMF’s superior performance. The framework detects drift within 18 seconds, restores F1 scores above 0.99 post-attack, reduces subgroup disparities by over 60%, and scales to over 2,300 requests per second with sub-50ms latency. These results validate HAMF’s design, demonstrating that embedding resilience and ethical alignment into the MLOps lifecycle is both effective and scalable.

The exponential growth of publications regarding the application of machine learning (ML) tools in medicine highlights the significant potential for ML to revolutionize the field. Despite the multitude of literature surrounding this topic, there are limited publications addressing the implementation and feasibility of ML models in clinical practice. Currently, Machine Learning Operations (MLOps), a set of practices designed to deploy and maintain ML models in production, is used in various information technology and industrial settings. However, the MLOps pipeline is not well researched in medical settings, where multiple barriers exist to implementing ML pipelines into practice. This study aims to detail how MLOps is implemented in health care and propose a maturity framework for the health care implementations. A scoping review search was conducted according to the Joanna Briggs Institute Manual for Evidence Synthesis. Results were synthesized using the 3-stage basic qualitative content analysis. We searched 4 databases (eg, MEDLINE, Embase, Web of Science, and Scopus) to include any studies that involved proof of concept or real-world implementation of MLOps in health care. Studies not reported in English were excluded. A total of 19 studies were included in this scoping review. The MLOps workflow identified within the studies included (1) data extraction (19/19 studies), (2) data preparation and engineering (18/19 studies), (3) model training (19/19 studies), (4) measured ML metrics and model evaluation (17/19 studies), (5) model validation and test in production (14/19 studies), (6) model serving and deployment (15/19 studies), (7) continuous monitoring (14/19 studies), and (8) continual learning (13/19 studies). We proposed a 3-stage MLOps maturity framework for health care based on existing studies in the field, that is, low (5/19 studies), partial (1/19 studies), and full maturity (13/19 studies). There were 8/19 studies that discussed ethical, legislative, and stakeholder considerations for MLOps implementations in health care settings. We investigated the implementation of MLOps in health care with a corresponding maturity framework. It is evident that only a limited number of studies reported the implementation of ML in health care contexts. Hence, it is imperative that we shift our focus toward creating an environment that supports the development of ML health care applications, such as improving existing data infrastructure, and engaging partners to support the development of MLOps applications. Specifically, we can include patients, policymakers, and health care professionals in the creation and implementation of ML applications. One of the main limitations includes the varying quality of each extracted study in terms of how the MLOps implementation was presented. Hence, it was difficult to verify the presence and discuss in depth all steps of the MLOps workflow for each study. Furthermore, due to the inherent nature of a scoping review protocol, there may be a compromise on an in-depth discussion of each step within the MLOps workflow.

Energy forecasting is critical for modern power systems, enabling proactive grid control and efficient resource optimization. However, energy forecasting projects require systematic approaches that span project inception to model deployment while ensuring technical excellence, domain alignment, regulatory compliance, and reproducibility. Existing methodologies such as CRISP-DM provide a foundation but lack explicit mechanisms for iterative feedback, decision checkpoints, and continuous energy-domain-expert involvement. This paper proposes a modular end-to-end framework for energy forecasting that integrates formal decision gates in each phase, embeds domain-expert validation, and produces fully traceable artifacts. The framework supports controlled iteration, rollback, and automation within an MLOps-compatible structure. A comparative analysis demonstrates its advantages in functional coverage, workflow logic, and governance over existing approaches. A case study on short-term electricity forecasting for a 2560 m2 office building validates the framework, achieving 24-h-ahead predictions with an RNN, reaching an RMSE of 1.04 kWh and an MAE of 0.78 kWh. The results confirm that the framework enhances forecast accuracy, reliability, and regulatory readiness in real-world energy applications.