Best Practices for Secure Model Deployment on AWS

International Journal of Computer Science and Engineering

© 2024 by SSRG - IJCSE Journal

Volume 11 Issue 5

Year of Publication : 2024

Authors : Rahul Bagai

10.14445/23488387/IJCSE-V11I5P102

How to Cite?

Rahul Bagai, "Best Practices for Secure Model Deployment on AWS," SSRG International Journal of Computer Science and Engineering , vol. 11,  no. 5, pp. 8-18, 2024. Crossref, https://doi.org/10.14445/23488387/IJCSE-V11I5P102

Abstract:

Security in deploying Machine Learning on Amazon Web Services requires critical security enrichments to guarantee a seamless transition. AWS offers a plethora of features and services that ensure secure model deployment. It includes organizations utilizing encryption, access management, and compliance to lay down a practical operational framework. This article highlights how using the services and features of AWS structures the deployment process in securely handling and modeling for safe data management. In light of GDPR or HIPAA, regulatory compliance should be considered for their impact on ML model functionality. AWS provides structured approaches to manage deployment through systematic advancements for correctly handling ML deployment and security protocols.

Keywords:

Machine Learning (ML), Amazon Identity Management (AIM), Access Control, Encryption, Amazon Web Services (AWS).

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