Year: 2022
Type: article
Abstract: MLOps has emerged as a critical practice for managing the lifecycle of AI/ML models, from development to deployment. This paper presents a comprehensive MLOps framework designed for automating model training, validation, deployment, and monitoring in cloud environments. The framework incorporates automated hyperparameter optimization, continuous integration/continuous deployment (CI/CD) pipelines, and scalable cloud-native tools to streamline the AI/ML lifecycle. Case studies demonstrate improved model reliability, faster deployment times, and reduced operational overhead. These advancements highlight the transformative potential of MLOps for enterprise-grade AI adoption.
Author: Srinivasa Subramanyam Katreddy
Language: English
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W7217590956