DEPLOYING AI/ML IN MICROSERVICES: A COMPARATIVE STUDY OF TOOLS AND METHODOLOGIES WITH SCALABILITY ANALYSIS AND CASE STUDIES OF LEADING COMPANIES

Abstract The integration of Artificial Intelligence and Machine Learning (AI/ML) in microservices architectures has gained significant traction due to its potential for scalability, flexibility, and efficiency in modern software development. This paper presents a comprehensive comparative study of various tools, frameworks, and methodologies for deploying AI/ML in microservices environments. It evaluates key factors such as performance, scalability, security, and ease of integration. Additionally, real-world case studies of leading companies implementing AI/ML in microservices are analyzed to highlight best practices, challenges, and solutions. A detailed scalability analysis is provided to assess how AI/ML models perform under different workloads in a microservices ecosystem. The findings of this study aim to guide practitioners and researchers in selecting the most suitable strategies for AI/ML deployment in microservices, ensuring optimal performance and resource utilization.