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

Mayank Jindal

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.

Keywords: Machine Learning Deployment, Cloud Computing, Scalability