Optimizing SAP Machine Learning-based Solutions through Custom API Integration

Abstract

Rapid changes, dynamic consumer preferences, and evolving market trends are the hallmarks of the business environment. SAP HANA has emerged as a potent platform to meet this demand due to its resilient foundation for real-time data analytics and processing and in-memory processing architecture. This research aims to improve anomaly detection capabilities by integrating machine learning (ML) models into the SAP HANA Fiori web application. This will be achieved by developing a custom Application Programming Interface (API). The proposed solution integrates ML models with SAP systems using FastAPI, providing real-time insights and decision-making capabilities, by leveraging scikit-learn's Local Outlier Factor (LOF) for anomaly detection. To guarantee seamless performance and scalability, the API is deployed on Azure using Docker containers. This paper illustrates the capability of custom APIs to integrate ML models into enterprise systems, enhance operational efficiency, and establish a reliable framework for real-time anomaly detection. The article addresses challenges associated with API integration, scalability, and system configuration, providing valuable insights for enhancing the deployment of machine learning in enterprise applications.