Cognitive Cloud Ecosystems for Industrial Foresight
DOI:
https://doi.org/10.5281/zenodo.21412780Keywords:
Cloud Native, Industrial Systems, Predictive Modeling, Data Ecosystems, Data Pipelines, Machine Learning, ML Lifecycle, Feature Stores, Real Time, Model Inference, Data Processing, Workflow Orchestration, Event Driven, Scheduling Systems, Cloud Computing, Data Infrastructure, Model Deployment, Decision Systems, Scalable Systems, Analytics Systems.Abstract
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.
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