ML Architecture Pattern Tool Selection
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Solution Overview
Problem
Conventional methods for determining architecture patterns and tools for applications are often manual and biased towards familiar tools from a single service provider, leading to suboptimal selections in terms of cost and performance.
Innovation Solution
The use of machine-learning based systems to determine use case-specific architecture patterns and tools by analyzing conversation data between users and virtual assistants, identifying optimal tool combinations based on optimization factors such as cost and performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual selection by solutions architect is used, then expertise and experience are leveraged, but tool selection is biased towards familiar tools from single service provider
Solution Approach 1:
The patent introduces an intermediary system (machine learning model) that mediates between the use case requirements and the tool selection. This intermediary analyzes conversation data, identifies functions, and recommends tools from multiple service providers without human bias, resolving the contradiction between leveraging expertise and achieving provider diversity.
Solution Approach 2:
The patent replaces the mechanical manual selection process with an automated machine learning-based system. The ML system processes conversation data, identifies functions, and selects tools algorithmically, eliminating human biases toward familiar tools while maintaining or improving selection quality through systematic analysis.
2Reliability
If manual selection process is used, then architect's knowledge is applied, but selection is limited to small number of familiar tools
Solution Approach 1:
The patent creates a universal tool recommendation system that handles multiple functions and use cases through a single ML model. The system can evaluate a large number of tools from different service providers for various functions (compute, storage, networking, etc.) without requiring separate manual evaluation processes, thus increasing both the quantity of tools considered and the accuracy of selection.
Solution Approach 2:
The patent replaces the limited manual review process with an automated ML system that can efficiently evaluate and compare a large number of tools across multiple dimensions (cost, performance, features) simultaneously, overcoming the human limitation of considering only a small number of familiar tools.
3Productivity
If conventional manual approach is used, then process is simple and quick, but cost and performance optimization is suboptimal
Solution Approach 1:
The patent performs preliminary analysis by training the ML model on conversation data and tool characteristics before actual tool selection. The pre-trained model can then quickly recommend optimized tools for new use cases, achieving both speed (like manual process) and optimization quality (superior to manual process) simultaneously.
Solution Approach 2:
The patent implements feedback mechanisms where the ML model continuously learns from conversation data, tool performance metrics, and cost information. This feedback loop enables the system to improve its recommendations over time, achieving better cost and performance optimization while maintaining quick selection speeds through automated processing.
Data Source
AI summary
An architecture pattern and tools may be determined for an application. For instance, conversation data between a user and a virtual assistant associated with a use case for an application may be received from a user device, and a plurality of functions to serve the use case may be identified based on the conversation data. An architecture pattern may be determined for the application based on the plurality of functions, where the architecture pattern may indicate a plurality of tool types for performing the plurality of functions. For each tool type of the plurality of tool types, a particular tool may be determined, from a plurality of tools associated with the respective tool type, to perform a corresponding function from the plurality of functions based on the conversation data. The architecture pattern and the particular tool determined for each tool type may be provided to the user device for display.