Self-learning IT Change Risk Prediction
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Solution Overview
Problem
Existing IT ticketing systems lack an effective mechanism to predict and prevent change-induced incidents in the IT environment, often resulting in unforeseen problems and outages.
Innovation Solution
A self-learning automated system that uses machine learning models to analyze change requests, classify them into risk categories, and predict the likelihood of causing problems in the IT environment, thereby preventing potentially harmful changes from being executed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated systems are used to process change tickets, then productivity is improved, but reliability deteriorates due to change-induced incidents
Solution Approach 1:
The system performs preliminary risk assessment and classification of change tickets before automated processing. Machine learning models analyze change requests, historical data, and contextual information to predict potential incidents, allowing preventive actions to be taken before changes are deployed to the IT environment.
Solution Approach 2:
The system incorporates feedback loops where outcomes of previous changes are continuously analyzed to improve prediction accuracy. Historical incident data and change outcomes are fed back into the machine learning models to refine risk assessment, creating a self-learning system that improves reliability while maintaining automated processing.
2Device complexity
If static risk assessment methods are used, then device complexity is reduced, but measurement precision deteriorates due to inability to account for temporal variations
Solution Approach 1:
The system transitions from static to dynamic risk assessment by implementing machine learning models that continuously learn from new data. The models adapt to temporal variations in change properties and incident patterns, improving measurement precision while maintaining manageable system complexity through automated learning processes.
Solution Approach 2:
The machine learning models perform self-learning and automatic model improvement without requiring manual reconfiguration. The system automatically adjusts to new patterns in change requests and incident data, maintaining high measurement precision while keeping the system structure relatively simple through autonomous adaptation.
3Reliability
If machine learning models are trained on historical data, then reliability is improved through better prediction, but loss of time increases due to training and processing requirements
Solution Approach 1:
The system performs preliminary training of machine learning models on historical data in advance, before production use. This allows the models to be pre-adapted to organizational patterns and incident types, reducing the time required for real-time processing while maintaining high prediction accuracy when changes are assessed.
Solution Approach 2:
The system uses partial retraining or incremental learning approaches where only necessary model components are updated with new data, rather than complete retraining. This reduces the time loss associated with model maintenance while keeping reliability high through continuous improvement of critical prediction capabilities.
Data Source
AI summary
Embodiments relate to providing self-learning automated information technology change risk prediction. A processor inputs a change request to a first machine learning model, the first machine learning model determining at least one word pair in the change request, the change request being a modification in an IT environment. The processor classifies the at least one word pair into a change category for the IT environment using a second machine learning model, the change category identifying a type of the modification to be executed in the IT environment. The processor determines a likelihood of causing a problem in the IT environment as a result of executing the modification. The processor automatically performs an action to prevent the modification of the change request in the IT environment.