ENGINEERING SCALABLE AND ADAPTIVE AI SYSTEMS: AN ML-OPS-DRIVEN FRAMEWORK FOR INNOVATION IN INTELLIGENT APPLICATIONS

Abstract

The rapid advancement of artificial intelligence (AI) has propelled the development of intelligent applications across various domains, from healthcare and finance to manufacturing and autonomous systems. However, building scalable and adaptive AI systems remains a significant challenge due to the complexity of managing large datasets, continuous model training, deployment, and real-time adaptation. This paper presents an MLOps-driven framework designed to streamline the development and deployment of AI systems by integrating machine learning operations (MLOps) principles into the lifecycle of intelligent applications. The proposed framework aims to enhance scalability, robustness, and adaptability of AI models by automating workflows, ensuring continuous integration, and facilitating seamless model versioning and monitoring. By focusing on key aspects such as model governance, data pipelines, and real-time feedback loops, the framework promotes innovation while addressing challenges related to model performance, maintenance, and deployment. This research underscores the importance of leveraging MLOps practices for fostering more reliable, efficient, and scalable AI-driven systems, making them better suited to meet the demands of rapidly evolving technological landscapes.