Hybrid Cloud Solutions for Machine Learning Deployment: A Framework for Security and Scalability

Hybrid cloud solutions have emerged as pivotal enablers for scalable, secure, and cost-efficient machine learning (ML) deployments, addressing the growing computational and data privacy challenges faced by organizations. This study evaluates three prominent hybrid cloud platforms—Azure Stack, Google Anthos, and AWS Outposts—based on performance metrics, resource utilization, security, scalability, and cost-efficiency. The results highlight Google Anthos as the most robust solution, delivering superior model accuracy, low latency, and high scalability while maintaining cost-effectiveness. Statistical analysis confirms significant differences across platforms, with Google Anthos demonstrating the highest cost-efficiency correlation. This research offers a comprehensive framework for selecting hybrid cloud solutions tailored to organizational needs and provides actionable insights into optimizing ML deployment strategies. Future work should explore the integration of advanced orchestration tools and emerging technologies for enhanced hybrid cloud performance.