Comparative Analysis of AWS Model Deployment Services

TL;DR

This paper compares AWS model deployment services, highlighting their strengths and weaknesses, and guides developers in choosing the most suitable service based on efficiency, flexibility, and scalability.

Contribution

It provides a comprehensive comparative analysis of AWS SageMaker, Lambda, and ECS, emphasizing their advantages and ideal use cases for model deployment.

Findings

Lambda excels in efficiency and autoscaling

ECS offers superior flexibility and infrastructure control

ECS is preferred for complex container environments and scalability

Abstract

Amazon Web Services (AWS) offers three important Model Deployment Services for model developers: SageMaker, Lambda, and Elastic Container Service (ECS). These services have critical advantages and disadvantages, influencing model developer's adoption decisions. This comparative analysis reviews the merits and drawbacks of these services. This analysis found that Lambda AWS service leads in efficiency, autoscaling aspects, and integration during model development. However, ECS was found to be outstanding in terms of flexibility, scalability, and infrastructure control; conversely, ECS is better suited when it comes to managing complex container environments during model development, as well as addressing budget concerns -- it is, therefore, the preferred option for model developers whose objective is to achieve complete freedom and framework flexibility with horizontal scaling. ECS is better suited to ensuring performance requirements align with project goals and constraints. The AWS service selection process considered factors that include but are not limited to load balance and cost-effectiveness. ECS is a better choice when model development begins from the abstract. It offers unique benefits, such as the ability to scale horizontally and vertically, making it the best preferable tool for model deployment.