ML Change Registry Vault for Enterprise IT Reliability
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Solution Overview
Problem
The complexity of enterprise computing environments makes it difficult and time-consuming to identify the potential direct and indirect impacts of changes, leading to unintended adverse effects on seemingly unrelated components, with IT personnel relying on intuition and having a low success rate in preventing change failures.
Innovation Solution
A system utilizing machine learning techniques to evaluate the likelihood of adverse operations by analyzing change characteristics, generating an adverse operation score, and comparing it to a threshold to block or allow changes, thereby preventing unforeseen adverse effects through a change registry vault and change release evaluation engine.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional manual evaluation process is used by IT personnel, then changes can be implemented with human judgment, but the success rate is low and time consumption is high due to complexity
Solution Approach 1:
The patent replaces the manual mechanical evaluation process with an automated machine learning system. The ML model automatically analyzes change characteristics, evaluates potential adverse effects, and predicts outcomes, substituting human judgment with algorithmic assessment. This automation resolves the contradiction by providing consistent, scalable evaluation that is both faster and more reliable than manual processes.
Solution Approach 2:
The patent introduces an intermediary machine learning system that acts as a mediator between change proposals and deployment. This intermediary automatically assesses changes by analyzing characteristics such as change type, scope, and historical data, providing an objective evaluation that complements or replaces human judgment. The intermediary resolves the time and reliability contradiction by processing evaluations consistently and efficiently.
2Reliability
If comprehensive evaluation of all changes is performed manually, then potential adverse effects can be identified, but the complexity and time required make this impractical for enterprise scale
Solution Approach 1:
The patent replaces complex manual evaluation with automated machine learning analysis. The system automatically processes change characteristics, historical data, and system state information to identify potential adverse effects. This substitution resolves the contradiction by providing comprehensive detection capability without the proportional increase in manual complexity and time requirements.
Solution Approach 2:
The patent uses machine learning models that learn from historical change data and system behavior patterns. By copying and generalizing from past experiences stored in training data, the system can evaluate new changes without manually analyzing every possible scenario. This approach provides comprehensive adverse effect detection while managing complexity through pattern recognition rather than exhaustive analysis.
3Reliability
If more manual review and evaluation is performed, then change failures can be prevented, but productivity and deployment speed decrease
Solution Approach 1:
The patent replaces slow manual review processes with automated machine learning evaluation. The ML system rapidly analyzes change characteristics and predicts potential failures, providing reliable assessment without the time delays of manual processes. This resolves the contradiction by maintaining high prevention capability while dramatically increasing deployment speed through automation.
Solution Approach 2:
The patent performs preliminary evaluation of changes using machine learning models before deployment. The system pre-assesses change characteristics, identifies potential adverse effects, and flags high-risk changes before they reach human reviewers or production systems. This preliminary action resolves the contradiction by catching failures early in the process, reducing the need for extensive manual review while maintaining high prevention rates.
Data Source
AI summary
A system for improving performance in an enterprise computing environment is disclosed and involves a change registry vault, including a machine learning model that analyzes whether or not implementation of a change will result in adverse operation of a component of the enterprise computing environment if the change is released, and takes different actions depending upon a result of that analysis. A corresponding method is also disclosed.