An Optimized Intelligent Open-Source MLaaS Framework for User-Friendly Clustering and Anomaly Detection

Abstract

In the big data era, businesses are in dire need of intelligent solutions to rapidly extract valuable insights. However, not all companies possess the specialized expertise required to operate machine learning algorithms. To bridge this gap, this paper introduces a cost-effective, user-friendly, dependable, adaptable, and scalable solution for visualizing, analyzing, processing, and extracting valuable insights from data. The proposed solution is an optimized open-source unsupervised machine learning as a service (MLaaS) framework that caters to both experts and non-experts in machine learning. The framework aims to assist companies and organizations in solving problems related to clustering and anomaly detection, even without prior experience or internal infrastructure. With a focus on several clustering and anomaly detection techniques, the proposed framework automates data processing while allowing user intervention. Furthermore, the proposed solution is expandable; it may include additional algorithms. It is versatile and capable of handling diverse datasets by generating separate rapid artificial intelligence (AI) models for each dataset and facilitating their comparison rapidly. The proposed framework provides a solution through a Representational State Transfer (RESTful) Application Programming Interface (API), enabling seamless integration with various systems. Real-world testing of the proposed framework on customer segmentation and fraud detection data demonstrates that it is reliable, efficient, cost-effective, and time-saving. With this innovative MLaaS framework, companies may harness the full potential of business analysis.