Advancements in Cloud-Based Machine Learning: Navigating Deployment and Scalability

Abstract

The widespread adoption of machine learning (ML) in various industries has brought to light significant challenges, particularly in deploying these complex models into production environments. The need for scalable, efficient, and robust solutions is paramount, and cloud computing emerges as a key player in this scenario. Cloud platforms offer the necessary infrastructure and tools to facilitate ML deployment, addressing issues like computational demand, data storage, and scalability. Within the cloud computing landscape, AWS SageMaker, a service provided by Amazon Web Services, has gained prominence. This paper undertakes a comprehensive review of the machine learning (ML) lifecycle within cloud-based platforms with a specific focus on AWS SageMaker. Additionally, this paper explores the critical aspect of scaling in ML deployment, analyzing both horizontal and vertical scaling methods within the context of cloud computing's dynamic resource management. This paper aims to deliver an in-depth analysis of the ML lifecycle in cloud platforms by elucidating the functionalities, benefits, and challenges of using AWS SageMaker in the broader spectrum of ML deployment and management.