Evaluating Mlops Toolchains: Performance And Cost Analysis On Major Cloud Providers

Abstract

This paper compares MLOps toolchains across the big Cloud providers, in terms of performance and cost. The study investigates case examples of Amazon and Vodafone through an explanatory research design by using secondary qualitative and quantitative data. Findings indicate that MLOps increases scalability, automates the ML pipelines, lowers infrastructure expenses, and enhances model deployment. Nevertheless, there are still problems with integration because of the low system maturity and lack of standardisation. It is suggested that organisations should invest in training and should use modular MLOps pipelines to implement it effectively.