AI-Driven Multi-Cloud Deployment Pipeline Automation
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Solution Overview
Problem
Existing deployment pipeline technologies are error-prone and inadequate for multi-cloud environments, failing to consider additional parameters and being inflexible due to the lack of machine learning and artificial intelligence, especially when dealing with codeless platforms and blockchain networks.
Innovation Solution
A system and method that utilizes AI and machine learning to structure application deployment pipelines by extracting data attributes, creating data serialization and configuration objects, and mapping blockchain network data elements, enabling dynamic validation of dependencies and version management across multiple cloud environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If standard source code templates are used for deployment pipelines, then the pipeline structure is simplified, but the system becomes error-prone and incapable of handling multi-cloud environments
Solution Approach 1:
The system changes the parameters of deployment pipelines by using AI/ML to dynamically generate and optimize pipeline configurations based on extracted data attributes from input data. This allows the pipeline to adapt to different cloud environments and application types, resolving the contradiction between simplified structure and reliable deployment.
Solution Approach 2:
The patent replaces manual mechanical pipeline configuration with AI/ML-based automated pipeline generation. The AI engine processes input data, extracts attributes, and automatically constructs deployment pipelines, eliminating human errors while maintaining structural simplicity.
2Extent of automation
If predefined relationships are used to determine pipeline optimization, then the optimization process is automated, but the system fails to handle codeless platforms and dynamic application development
Solution Approach 1:
The system enables self-service optimization by using AI/ML to automatically detect changes in application artifacts and generate optimized pipelines without predefined relationships. The AI engine analyzes the actual application structure and dynamically creates appropriate deployment pipelines, allowing the system to adapt to codeless platforms and dynamic development.
Solution Approach 2:
The patent introduces dynamics to the pipeline optimization process by making the pipeline generation adaptive and flexible. The AI-based system continuously learns from application changes and adjusts pipeline configurations in real-time, rather than relying on static predefined relationships.
3Productivity
If similarity algorithms are used to create deployment pipelines, then existing pipelines can be replicated, but the system introduces boundary effects and fails to accurately represent multi-cloud differences
Solution Approach 1:
The patent replaces traditional similarity algorithms with AI/ML-based data processing and attribute extraction. This substitution eliminates the boundary effects inherent in tree kernel algorithms while maintaining the ability to quickly create pipelines by learning from existing successful deployments.
Solution Approach 2:
The system changes the approach to pipeline creation by focusing on extracting and processing data attributes rather than relying on similarity matching. This parameter-driven approach allows for more accurate representation of multi-cloud differences while maintaining high productivity through automated AI-based generation.
4Quantity of substance
If data-driven approaches are used to determine pipeline similarity, then the system can process large datasets, but the approach becomes extremely error-prone in structuring deployment pipelines
Solution Approach 1:
The patent replaces purely data-driven similarity approaches with AI/ML-based attribute extraction and processing. This substitution maintains the ability to process large datasets while improving reliability by using intelligent algorithms that understand the semantic meaning of deployment parameters and can correctly structure pipelines even with complex multi-cloud configurations.
Data Source
AI summary
The present invention provides a system and a method for managing and deploying one or more applications in a multi-cloud environment. The invention structures an application pipeline for multiple cloud environments and creates a library of objects based on processing of one or more application pipeline historical data. The data models generated based on the historical data enable processing of a received data to execute a task of deploying one or more applications. The invention maps blockchain network-based data elements of one or more applications in a multi-cloud environment.