Comparative Analysis of AWS Model Deployment Services |
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| © 2024 by IJCTT Journal | ||
| Volume-72 Issue-5 |
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| Year of Publication : 2024 | ||
| Authors : Rahul Bagai | ||
| DOI : 10.14445/22312803/IJCTT-V72I5P113 | ||
How to Cite?
Rahul Bagai, "Comparative Analysis of AWS Model Deployment Services," International Journal of Computer Trends and Technology, vol. 72, no. 5, pp. 102-110, 2024. Crossref, https://doi.org/10.14445/22312803/IJCTT-V72I5P113
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 developers’ 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.
Keywords
AWS ECS, AWS Lambda, AWS SageMaker, Cost Analysis, Machine Learning Deployment, Performance Evaluation, scalability.
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