AI and ML Integration in Azure Cloud: Scalable Model Deployment and Real-Time Analytics for Intelligent Applications

Dadi, Chaitanya Bharat (2025) AI and ML Integration in Azure Cloud: Scalable Model Deployment and Real-Time Analytics for Intelligent Applications. World Journal of Advanced Research and Reviews, 26 (2). pp. 2654-2673. ISSN 2581-9615

The integration of Artificial Intelligence (AI) and Machine Learning (ML) technologies with Microsoft Azure Cloud provides a comprehensive framework for organizations seeking scalable, secure solutions for intelligent applications. Azure's suite of services encompasses the complete AI/ML lifecycle, from model development and deployment through Azure Machine Learning to real-time analytics via Azure Synapse and pre-built cognitive capabilities. This paper explores architectural considerations, implementation patterns, and practical strategies for leveraging these technologies in enterprise environments. Through examination of case studies across manufacturing, financial services, healthcare, and retail sectors, the document demonstrates how Azure's integrated ecosystem accelerates time-to-market while improving operational efficiency. Challenges including data privacy, model drift, governance requirements, and technical limitations are addressed alongside future directions such as edge AI deployment, federated learning, multi-cloud strategies, and quantum computing integration. The exploration reveals how these technologies are revolutionizing operations through predictive maintenance, fraud detection, sentiment analysis, and intelligent automation while emphasizing responsible implementation practices that balance innovation with ethical considerations.