Bridging MLOps and iPaaS: A Unified Framework for Governance and Observability in AI-Augmented Enterprise Integration
Abstract
The rapid proliferation of artificial intelligence capabilities within Integration Platform as a Service (iPaaS) solutions has fundamentally altered the enterprise integration landscape, enabling intelligent data routing, anomaly detection, predictive transformation, and automated pipeline optimization. However, this convergence of AI and integration introduces significant governance and observability challenges that existing MLOps practices and iPaaS operational models address only in isolation, leaving a critical gap in unified oversight. This paper presents the MLOps-iPaaS Governance Framework (MIPGF), a unified framework bridging MLOps model lifecycle management with iPaaS operational governance through four pillars: model governance and versioning, real-time observability of AI-driven decision points, compliance and auditability, and drift detection. We analyze governance gaps in three leading iPaaS platforms — MuleSoft Anypoint, Azure Integration Services, and Boomi — and evaluate the framework against enterprise integration scenarios in healthcare, financial services, and retail. Results demonstrate measurable improvements in anomaly detection, compliance posture, and incident resolution time, providing a practical governance blueprint for enterprises adopting AI-augmented integration
Article Information
Journal |
International Journal of Science, Research and Technology |
|---|---|
Volume (Issue) |
Vol. 6 No. 6 (2023): International Journal of Science, Research and Technology (IJSRAT) |
DOI |
|
Pages |
11080-11084 |
Published |
December 13, 2023 |
| Copyright |
All rights reserved |
Open Access |
This work is licensed under a Creative Commons Attribution 4.0 International License. |
How to Cite |
Tejaswi Bharadwaj Katta (%2023). Bridging MLOps and iPaaS: A Unified Framework for Governance and Observability in AI-Augmented Enterprise Integration. International Journal of Science, Research and Technology , Vol. 6 No. 6 (2023): International Journal of Science, Research and Technology (IJSRAT) , pp. 11080-11084. https://doi.org/10.15662/IJSRAT.2023.0606007 |
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