AI Deployment System for Cloud Infrastructure Automation
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Solution Overview
Problem
Existing computing environments face challenges in efficiently deploying and managing computing resources, particularly due to complexities in use and management, which hinder quick and cost-effective provisioning of cloud infrastructure.
Innovation Solution
A computer-implemented system for automated deployment of a computing environment, which includes a processing subsystem that receives a natural language description from a user, generates follow-up questions, and transforms the input into a prompt for a generative model to generate a deployment data structure defining deployment parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional manual deployment methods are used, then deployment control and customization are improved, but deployment time and complexity increase
Solution Approach 1:
The patent replaces manual mechanical deployment processes with an AI-based automated system. The generative model automatically creates deployment configurations from natural language inputs, eliminating the need for manual configuration of cloud resources, networking, and security settings while maintaining deployment control through the AI's understanding of requirements.
Solution Approach 2:
The system enables self-service deployment where the AI assistant autonomously generates and executes deployment configurations without requiring human intervention in the technical deployment process. The system independently translates user requirements into actionable deployment plans and executes them automatically.
2Manufacturing precision
If specialized technical assistance is provided, then deployment accuracy is improved, but cost and accessibility worsen
Solution Approach 1:
The patent introduces an AI assistant as an intermediary between the user and the complex deployment system. This intermediary translates simple natural language requirements into accurate technical deployment configurations, providing expert-level deployment accuracy while maintaining ease of use for non-expert users.
Solution Approach 2:
The system replaces the need for human expert intervention with an AI-based automated system that provides equivalent or superior deployment accuracy. The generative model has been trained to understand and execute deployment requirements with high precision, eliminating the need for specialized technical assistance while maintaining accuracy.
3Reliability
If comprehensive deployment parameters are collected, then deployment completeness is improved, but user input complexity increases
Solution Approach 1:
Instead of requiring users to provide comprehensive technical parameters, the system inverts the approach by having the AI generate complete deployment configurations from minimal natural language inputs. The AI proactively asks clarifying questions and fills in technical details that users would not typically know, transforming a complex input requirement into a simple conversational interface.
Solution Approach 2:
The system employs feedback mechanisms where the AI assistant asks follow-up questions to clarify requirements and ensures all necessary deployment parameters are captured. This iterative feedback process guarantees deployment completeness while keeping the initial user input simple and accessible.
Data Source
AI summary
Disclosed herein are methods and systems for automated deployment of a computing environment. A natural language description of a target computing environment is received from a user. A follow-up question to the user regarding a requirement of the target computing environment is generated. The natural language description and a response to the follow-up question are transformed to a prompt for a generative model, the prompt requesting a deployment data structure defining deployment parameters of the target computing environment.