Machine Learning-Enhanced Data Exchange with Azure Application Programming Interface Management

The incorporation of Machine Learning (ML) into Azure Application Programming Interface (API) Management creates an advanced platform for effective and intelligent data interchange. This method meets the increasing need for smooth, scalable, and secure API connection in contemporary corporate systems. The platform uses machine learning algorithms to provide predictive analytics, anomaly detection, and dynamic API scalability according to consumption trends. Azure API Management provides centralized oversight of APIs, enabling automated policy enforcement, versioning, and monitoring features, which are augmented by machine learning algorithms to guarantee optimized speed and contextual response. These attributes enable the system to customize user experiences, intelligently prioritize traffic, and adaptively manage resources. Augmented security protocols, encompassing danger identification and traffic pattern assessment, moreover, facilitate dependable data management. The increasing need for real-time, data-driven choices highlights the innovative integration of Azure's API functionalities with machine learning technology. This integration fosters creativity, expedites digital transformation, and enhances operational agility across diverse sectors and services reliant on substantial data interchange.

Authors

Institutions

Publication Details

Published
2025-06-05
DOI
https://doi.org/10.1109/iccsp64183.2025.11088511
Primary Topic
Software System Performance and Reliability
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Machine Learning-Enhanced Data Exchange with Azure Application Programming Interface Management

Subramani Roychoudri, R Monisha, not provided Currie B.J., Karthikeyan Marappan et al.
Software System Performance and Reliability
article

Machine Learning-Enhanced Data Exchange with Azure Application Programming Interface Management

Subramani Roychoudri, R Monisha, not provided Currie B.J., Karthikeyan Marappan, Ramchandar Rao R, Shanmugapriya Inbarajan
article en

Abstract

The incorporation of Machine Learning (ML) into Azure Application Programming Interface (API) Management creates an advanced platform for effective and intelligent data interchange. This method meets the increasing need for smooth, scalable, and secure API connection in contemporary corporate systems. The platform uses machine learning algorithms to provide predictive analytics, anomaly detection, and dynamic API scalability according to consumption trends. Azure API Management provides centralized oversight of APIs, enabling automated policy enforcement, versioning, and monitoring features, which are augmented by machine learning algorithms to guarantee optimized speed and contextual response. These attributes enable the system to customize user experiences, intelligently prioritize traffic, and adaptively manage resources. Augmented security protocols, encompassing danger identification and traffic pattern assessment, moreover, facilitate dependable data management. The increasing need for real-time, data-driven choices highlights the innovative integration of Azure's API functionalities with machine learning technology. This integration fosters creativity, expedites digital transformation, and enhances operational agility across diverse sectors and services reliant on substantial data interchange.

National Institute of Technology Warangal (IN), Saint Joseph's College (US), Rama University (IN), St. Joseph's Institute of Technology (IN), Division of Human Resource Management (US), Technical Solutions (United States) (US)
Openalex Percentile: Top 79%
Software System Performance and Reliability
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.