Adaptive FinOps Guidelines for Accurate Cloud Maturity Assessment
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Solution Overview
Problem
Existing FinOps practices rely on manual assessments that are prone to errors and oversights, using generic guidelines that are not tailored to individual clients, leading to inefficient and inaccurate evaluations of cloud resource usage and cost optimization.
Innovation Solution
An adaptive guideline customization and profiling system using machine learning techniques, including large language models, to generate customized FinOps guidelines and assessments based on client-specific data, historical assessments, and expert knowledge, providing tailored recommendations for improving cloud resource management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual assessments are used to evaluate cloud resource usage, then human expertise and judgment can be applied, but the process is prone to errors and oversights and is time-consuming
Solution Approach 1:
The patent replaces manual mechanical assessment processes with an automated system that uses machine learning models and algorithms to evaluate cloud resource usage. The system automatically collects data, processes it through trained models, and generates assessments without human intervention, eliminating human errors while maintaining high accuracy and reducing time consumption.
Solution Approach 2:
The assessment system performs self-evaluation by automatically collecting cloud usage data, processing it through machine learning models, and generating maturity assessments without requiring manual human analysis. The system serves itself by autonomously completing the entire assessment workflow from data collection to recommendation generation.
2Productivity
If generic guidelines are used for FinOps assessments, then the assessment process is simple and fast, but the evaluations are not tailored to individual clients and lack precision
Solution Approach 1:
The patent applies local quality by customizing assessment guidelines for each individual client based on their specific cloud usage patterns, organizational structure, and FinOps maturity level. The system generates client-specific recommendations rather than applying uniform generic guidelines, ensuring high precision and relevance for each assessment while maintaining automated efficiency.
Solution Approach 2:
The assessment guidelines are dynamic and adapt to each client's unique characteristics. The system adjusts the assessment criteria, weightings, and recommendations based on real-time analysis of client data, allowing the guidelines to evolve and customize themselves for each assessment rather than remaining static and generic.
3Measurement precision
If detailed client-specific data collection is performed to improve assessment accuracy, then tailored recommendations can be provided, but the complexity of the assessment system increases
Solution Approach 1:
The patent segments the assessment system into distinct modular components: data collection modules that gather specific client information, processing modules that analyze the data through machine learning models, and output modules that generate recommendations. This segmentation allows the system to handle detailed client-specific data efficiently without overwhelming complexity, as each module performs a specific function independently.
Solution Approach 2:
The system introduces intermediary machine learning models that act as mediators between raw client data and assessment conclusions. These models process and interpret detailed client-specific data, transforming complex information into structured insights that drive accurate assessments and recommendations, thereby managing system complexity through intelligent intermediation.
Data Source
AI summary
A computer-implemented method includes: generating, by a computing device, a set of customized guidelines using at least user data and a current set of known guidelines; improving, by the computing device, the set of customized guidelines using a refined customer profile including user data of a current client; providing, by the computing device, a maturity assessment of the current client using the improved set of customized guidelines and known maturity assessments; and providing a recommendation to the current client based on the improved set of customized guidelines and maturity assessment.