Benefits and Challenges of Deploying Machine Learning Models in the Cloud
Keywords:
machine learning; cloud computing; model deployment; scalability; data security; performance optimizationAbstract
The integration of machine learning (ML) models with cloud computing has revolutionized the way organizations process and analyze data. This paper explores the multifaceted benefits and challenges associated with deploying ML models in cloud environments. Through a comprehensive review of current literature and industry practices, we examine the scalability, cost-effectiveness, and flexibility offered by cloud-based ML deployments. Simultaneously, we address the complexities surrounding data security, model performance, and regulatory compliance. Our analysis includes case studies from various sectors, providing insights into real-world implementations and their outcomes. The paper concludes with recommendations for best practices and future research directions, aiming to guide both academics and practitioners in optimizing cloud-based ML deployments.
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