Framework for failure detection, forecasting and remediation combining domain-specific telemetry and customized machine learning models

Abstract

Techniques for providing a centralized framework for forecasting IT component failures. The techniques include collecting raw telemetry data specific to different IT component domains, and transforming the telemetry data into structured telemetry data. The techniques include performing feature engineering on the structured telemetry data to obtain features relevant to IT component failures in each IT component domain, and, for each IT component domain, using the features to generate a customized ML model. The techniques include accessing features relevant to IT component failures in each IT component domain, accessing a customized ML model for forecasting IT component failures in the IT component domain, and forecasting IT component failures in the IT component domain using the customized ML model. By providing a centralized framework for forecasting IT component failures that combines the use of domain-specific telemetry data with customized ML models, improved fault resilience and reduced system downtime can be achieved.