ML-Based DevOps Platform Provisioning
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Solution Overview
Problem
Current techniques for implementing software development and information-technology operations (DevOps) platforms are time-consuming and resource-intensive, leading to wastage of computing resources, networking resources, and human resources due to manual processing and misconfigurations.
Innovation Solution
A provisioning platform that dynamically generates and provisions a customized platform for selected applications, tools, and artificial intelligence assets using a machine learning model to identify conflicting or redundant components, determine required computing resources, and create a customized template for automatic provisioning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual processing and configuration methods are used for DevOps platforms, then flexibility and control are maintained, but time consumption and resource wastage increase significantly
Solution Approach 1:
The system performs preliminary actions by pre-configuring platform templates with commonly used applications, tools, and AI assets. When a user requests a DevOps platform, the system automatically provisions it from pre-configured templates, eliminating the need for manual step-by-step configuration and significantly reducing implementation time.
Solution Approach 2:
The system enables self-service by automatically detecting user requirements from input data, selecting appropriate platform components, and provisioning the customized DevOps platform without requiring manual intervention. The system autonomously identifies conflicting or redundant components and adjusts the configuration accordingly.
2Reliability
If manual configuration of DevOps platforms is performed, then customization is possible, but resource wastage and misconfigurations occur
Solution Approach 1:
The system incorporates feedback mechanisms by automatically analyzing the input data to identify conflicting or redundant applications, tools, and AI assets. The machine learning model processes this feedback to optimize the platform configuration, ensuring compatibility and eliminating potential misconfigurations before deployment.
Solution Approach 2:
The system replaces manual mechanical configuration processes with automated machine learning-based provisioning. The ML model automatically analyzes requirements, selects appropriate components, and configures the platform, eliminating human errors and resource wastage associated with manual configuration.
3Adaptability or versatility
If comprehensive applications, tools, and AI assets are included in the platform, then functionality and versatility improve, but complexity and resource requirements increase
Solution Approach 1:
The system segments the DevOps platform into modular components including separate applications, tools, and AI assets. This segmentation allows the system to selectively provision only the necessary components based on user requirements, reducing complexity while maintaining versatility. The modular structure enables independent configuration and management of each component.
Solution Approach 2:
The system implements universality by creating a standardized platform template that can serve multiple purposes and support various DevOps workflows. The template includes commonly used applications, tools, and AI assets that can be configured for different scenarios, reducing the need for multiple specialized configurations while maintaining adaptability.
Data Source
AI summary
A device may receive, from the client device, provisioning data selected from a user interface and identifying a cloud provider or on premise resources, an infrastructure, applications, tools, and artificial intelligence (AI) assets for provisioning a customized platform. The device may process the provisioning data, with a machine learning model, to identify conflicting or redundant applications, tools, or AI assets and to generate updated provisioning data, and may determine computing resource data identifying computing resources required for execution of the applications, tools, and AI assets. The device may obtain the applications, tools, and AI assets from a data structure, and may generate a customized template based on the computing resources and the applications, tools, and AI assets. The device may execute the customized template to provision the computing resources with the applications, tools, and AI assets and to create the customized platform.