Low-Code Cloud Deployment Using AI-Generated Service And IaC Scripts
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional software development for cloud platforms is complex, requiring extensive manual efforts and technical expertise, leading to delays and inefficiencies, especially for non-technical stakeholders.
Innovation Solution
A method and system for automated low-code no-code model deployment using a trained model to generate recommendations from a service catalog database, selecting services, generating infrastructure and application scripts, and deploying application code in a target environment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional software development approaches are used for cloud platforms, then technical expertise and manual efforts are required, but this leads to complexity, delays, and inefficiencies
Solution Approach 1:
The system enables self-service automated deployment by allowing non-technical users to input natural language requirements, which the AI model then processes to generate and execute deployment configurations automatically, eliminating the need for manual technical expertise
Solution Approach 2:
The patent replaces manual mechanical coding and configuration processes with an AI-based automated system that generates deployment scripts and configurations through machine learning models, substituting human manual efforts with intelligent automation
2Loss of time
If manual coding and architectural decisions are made, then control and customization are achieved, but this results in time-consuming and error-prone processes
Solution Approach 1:
The system performs preliminary actions by pre-training AI models on cloud architecture patterns and deployment best practices, enabling the automated system to make accurate architectural decisions and generate error-free deployment configurations without manual intervention
Solution Approach 2:
The system incorporates feedback mechanisms where the AI model learns from deployment outcomes and continuously improves its recommendations, reducing errors over time while maintaining rapid automated deployment cycles
3Ease of operation
If extensive documentation and prior knowledge are required for cloud development, then informed decisions can be made, but this creates barriers for non-technical stakeholders
Solution Approach 1:
The AI model acts as an intermediary that translates natural language requirements from non-technical users into technical deployment configurations, bridging the gap between user intent and cloud infrastructure requirements without requiring users to understand technical documentation
Solution Approach 2:
The system copies and adapts proven cloud architecture patterns and deployment templates from existing documentation and best practices into automated configurations, making expert knowledge accessible to all users without requiring them to study the original technical materials
Data Source
AI summary
A method and a system for automatically deploying a low-code no-code model are disclosed. The method includes receiving an input in at least one format. The method includes analyzing, using a trained model, the input to generate at least one recommendation. The method includes selecting at least one service from the service catalog database based on the generated at least one recommendation. The method includes generating an infrastructure script for the selected at least one service using an Infrastructure as Code (IAC) catalog database having a plurality of pre-defined IAC scripts. The method includes generating an application script for the generated infrastructure script using a code catalog database having a plurality of application scripts. The method includes generating an application code for deployment based on an integration of the selected at least one service and the generated application script.