Automated Cloud Application Deployment via ML Knowledge Model
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Solution Overview
Problem
Conventional manual application deployment in cloud computing environments is time-consuming, requires extensive administrative knowledge, and is prone to errors, necessitating a more efficient and automated solution.
Innovation Solution
An automated application deployment method using a machine learning knowledge model that analyzes deployment requests, determines installation prerequisites, validates target environments, and facilitates deployment plans, with the option to apply artificial intelligence for issue resolution and manual overrides for feedback adaptation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual application deployment is performed, then deployment can be completed with basic system access, but it requires substantial time expenditure and extensive administrative knowledge
Solution Approach 1:
The system performs self-service by automatically analyzing deployment requests, determining installation prerequisites, validating target environments, and executing deployment plans without requiring extensive administrative knowledge or manual intervention from subject matter experts
Solution Approach 2:
Manual mechanical deployment processes are replaced with an automated machine learning-based system that uses AI models to analyze configuration details, validate environments, and execute deployments, substituting human administrative actions with automated intelligent systems
2Reliability
If manual application deployment is performed by subject matter experts, then deployment accuracy can be maintained, but it requires extensive administrative knowledge and increases potential for human error
Solution Approach 1:
Human administrative actions are replaced with an automated machine learning system that consistently applies validation rules and deployment procedures, eliminating human error while maintaining deployment reliability through systematic automated validation of target environments
Solution Approach 2:
The system incorporates feedback mechanisms where the machine learning model analyzes deployment requests, validates environments, and adjusts deployment plans based on validation results, creating a closed-loop system that improves reliability through continuous validation and adaptation
3Productivity
If automated deployment is implemented using machine learning, then deployment efficiency is improved and human intervention is minimized, but the system requires validation and adaptation mechanisms
Solution Approach 1:
The machine learning model incorporates feedback from deployment outcomes and environment validation results to continuously improve its deployment decisions, with subject matter experts validating and adapting the model to ensure accuracy while maintaining high productivity
Solution Approach 2:
The system performs preliminary validation of target environments before deployment execution, analyzing configuration details and checking prerequisites in advance to prevent deployment failures and reduce the need for corrective interventions
Data Source
AI summary
Techniques are described relating to automated deployment of an application in a managed services domain of a cloud computing environment. One or more of such techniques may minimize human interaction or intervention during deployment of the application. An associated method includes receiving from a client system a request to deploy the application in a target environment and analyzing the request via a machine learning knowledge model. Additionally, the method includes requesting from the client system access to the target environment and, upon receiving access to the target environment, validating the target environment through inspection. Upon validating the target environment, the method further includes facilitating presentation of an application deployment plan through an interface of the client system. Responsive to client approval of the application deployment plan, the method further includes deploying the application by facilitating application installation in the target environment via the machine learning knowledge model.