Cloud Based Machine Learning Architecture for Scalable Intelligent Enterprise Observability and Automated Telemetry Analysis

Abstract

Modern enterprises increasingly depend on distributed cloud applications, microservices, containers, serverless platforms, APIs, and hybrid infrastructures, creating highly complex operational environments that generate massive volumes of logs, metrics, traces, events, and application telemetry. Traditional observability approaches often depend on manually defined rules, static thresholds, and fragmented monitoring platforms, limiting their ability to identify complex anomalies and predict infrastructure failures. This paper proposes a cloud-based machine learning architecture for scalable intelligent enterprise observability and automated telemetry analysis. The proposed architecture integrates cloud-native data collection, centralized telemetry ingestion, distributed processing, machine learning-based anomaly detection, predictive analytics, automated event correlation, and intelligent alert prioritization. Telemetry collected from heterogeneous enterprise resources is normalized and processed through scalable cloud services before being analyzed using supervised, unsupervised, and deep learning models. Feature engineering techniques identify behavioral patterns across infrastructure, applications, networks, databases, and APIs, while machine learning models detect abnormal operational conditions and predict potential service degradation. An intelligent correlation layer reduces duplicate alerts by associating related events across distributed components. The architecture also incorporates automated remediation interfaces and feedback mechanisms for continuous model improvement. The proposed framework aims to improve observability scalability, anomaly detection accuracy, operational efficiency, incident response, and enterprise service reliability while reducing monitoring complexity and unnecessary operational overhead.