Cloud-Native AI Across Industries: Healthcare, Finance, and Manufacturing

This article evaluates the transformative impact of cloud-native data engineering and AI/ML technologies across healthcare, finance, and manufacturing sectors, examining how these industries leverage sophisticated architectures to address domain-specific challenges while driving operational excellence. Through comprehensive evaluation of documented implementations, including predictive diagnostics platforms, federated learning systems, real-time fraud detection mechanisms, explainable credit scoring models, predictive maintenance solutions, and AI-driven supply chain optimization, the study reveals both convergent architectural patterns and industry-specific adaptations that enable successful deployment at scale. The research identifies the medallion architecture as a dominant organizing principle for data pipelines, highlights the emergence of streaming-first approaches across traditionally batch-oriented domains, and demonstrates how feature stores and standardized MLOps practices facilitate cross-project reusability and accelerated innovation. While technological convergence around platforms like Databricks, Snowflake, Azure ML, and Kubernetes simplifies talent acquisition and knowledge transfer, each sector maintains unique requirements: healthcare prioritizes privacy-preserving techniques and clinical validation, finance emphasizes model explainability and sub-millisecond latency, and manufacturing balances edge-cloud hybrid architectures with long equipment lifecycles. These findings provide actionable insights for practitioners implementing cloud-native AI/ML solutions, emphasizing that success requires not only technological sophistication but also careful consideration of regulatory compliance, organizational readiness, and strategic alignment with business objectives.