ML-Based DevOps Platform Provisioning Automation
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Solution Overview
Problem
Current techniques for implementing DevOps platforms are time-consuming and resource-intensive, leading to waste of computing, networking, and human resources due to manual processing of infrastructure, applications, and tools, as well as misconfigurations and their correction.
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, optimizing technical components and reducing irrelevant or inconsistent combinations, thereby conserving resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual processing techniques are used for implementing DevOps platforms, then flexibility and control are maintained, but time consumption and resource waste increase
Solution Approach 1:
The system performs self-service by automatically analyzing provisioning requests, identifying required infrastructure components, and configuring the platform without human intervention. The machine learning model autonomously processes the entire provisioning workflow, eliminating manual processing steps and significantly reducing time consumption while maintaining high flexibility through adaptive component selection.
Solution Approach 2:
Manual mechanical processing is replaced by an automated machine learning-based system. The ML model substitutes human operators in analyzing requirements, selecting infrastructure components, and configuring platforms, thereby eliminating the time-consuming manual steps while preserving the ability to handle diverse and flexible provisioning scenarios through intelligent automation.
2Reliability
If comprehensive infrastructure components are manually configured, then platform functionality is ensured, but resource intensity and complexity increase
Solution Approach 1:
The machine learning model incorporates feedback mechanisms by continuously learning from provisioning outcomes and configuration results. This feedback loop enables the system to automatically adjust its component selection and configuration strategies, ensuring reliable platform functionality while simplifying the process through experience-based optimization rather than complex manual procedures.
Solution Approach 2:
The system dynamically changes parameters such as infrastructure component selection, configuration settings, and resource allocation based on the specific provisioning request and learned patterns. This adaptive parameter adjustment ensures reliable platform deployment for diverse scenarios while avoiding the need for complex manual configuration procedures, as the ML model automatically optimizes parameters for each case.
3Productivity
If automated provisioning is implemented, then speed and efficiency are improved, but accuracy in identifying relevant components may deteriorate
Solution Approach 1:
The machine learning model performs preliminary analysis of provisioning requests by pre-processing and understanding the requirements before actual platform deployment. This preliminary action enables the system to accurately identify relevant infrastructure components in advance, ensuring both high automation speed and precise component selection by preparing the configuration plan before execution.
Solution Approach 2:
The machine learning model acts as an intermediary between the provisioning request and the actual platform deployment. It mediates the process by intelligently analyzing requirements, selecting appropriate infrastructure components, and generating configuration plans, thereby bridging the gap between automated speed and selection accuracy through intelligent decision-making rather than simple automation.
Data Source
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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.