ML-Driven Cloud Architecture Scanning for Automated Deployment
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Solution Overview
Problem
Cloud data and technology solution delivery is costly and time-consuming due to manual processes dependent on expert talent, with high risks from human errors and inefficient data management, and conventional techniques fail to leverage institutional knowledge or provide systematic approaches for complex deployments across multi-cloud and hybrid environments.
Innovation Solution
A computer-implemented method using machine learning and artificial intelligence to scan existing computing environments, collect data through questionnaires, and analyze current and future architecture states using descriptive, predictive, diagnostic, or prescriptive analytics models to generate automated deployment options, thereby centralizing knowledge and improving efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual design, development, test, and delivery processes are used for cloud data and technology solution delivery, then expert engineering talent can provide customized solutions, but the process becomes costly and time-consuming
Solution Approach 1:
The system enables self-service through automated environmental scanning, data collection via questionnaires, and ML-driven deployment option generation. The platform autonomously assesses current computing environments, collects future state requirements, and generates migration strategies without requiring expert engineering intervention for each project
Solution Approach 2:
The system performs preliminary actions by pre-scanning computing environments to collect architecture state data, pre-training ML models on institutional knowledge, and pre-generating deployment options based on analyzed data. This preparatory work enables faster delivery without sacrificing solution quality
2Reliability
If manual processes are used for data curation and data management, then data accuracy and governance can be maintained, but human errors occur due to lack of knowledge and execution
Solution Approach 1:
The system replaces manual mechanical processes with automated ML-driven operations. ML models analyze architecture data, generate deployment options, and provide recommendations, eliminating human execution errors while maintaining data accuracy through systematic automated processes
Solution Approach 2:
The system implements feedback loops where ML models continuously learn from analyzed data and outcomes. The platform provides feedback on data quality, validates architecture states, and refines deployment recommendations based on project outcomes, thereby maintaining accuracy while reducing errors
3Measurement precision
If empirical data shows 70% of budgets are consumed by data readiness operations, then comprehensive data assessment can be performed, but costs become excessively high
Solution Approach 1:
The system performs self-service environmental scanning that automatically collects architecture state data without requiring extensive manual data readiness operations. The automated scanning and ML analysis reduce the 70% budget consumption by eliminating manual data collection and assessment activities
Solution Approach 2:
The system changes parameters by using ML models to analyze data more efficiently. Instead of exhaustive manual assessment, the ML-driven approach achieves comprehensive evaluation with reduced resource consumption by optimizing analysis parameters and using intelligent sampling
4Ease of operation
If conventional static visualization techniques are shared across organizations, then standardization is achieved, but users cannot apply filters and visualizations do not update to keep pace with changes in data over time
Solution Approach 1:
The system transforms static visualizations into dynamic, interactive displays that automatically update as new data is collected from environmental scanning. Users can apply filters and the visualizations adapt in real-time, combining standardization with versatility through ML-driven dynamic content generation
5Device complexity
If conventional technologies do not leverage institutional knowledge, then simpler processes can be used, but systematic approaches for complex multi-cloud and hybrid cloud deployments are not provided
Solution Approach 1:
The system performs preliminary action by pre-training ML models on institutional knowledge from previous multi-cloud and hybrid cloud deployments. This stored knowledge is then applied systematically to new projects, providing complex deployment capabilities while maintaining process simplicity through automated model-driven operations
Data Source
AI summary
A method includes collecting current data and architecture state, collecting future data and architecture state; analyzing the current and/or future data and architecture state to generate deployment options; and causing the summary of options to be displayed. A computing system includes a processor and a memory comprising instructions, that when executed, cause the system to collect current data and architecture state, collect future data and architecture state; analyze the current and/or future data and architecture state to generate deployment options; and cause the summary of options to be displayed. A non-transitory computer-readable storage medium includes executable instructions that, when executed by a processor, cause a computer to collect current data and architecture state, collect future data and architecture state; analyze the current and/or future data and architecture state to generate deployment options; and cause the summary of options to be displayed.