Edge Computing Configuration for Platform-Agnostic ML Deployment
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Solution Overview
Problem
The integration of machine learning software on edge computing platforms faces challenges due to fragmented workflows and technical knowledge gaps between diverse roles, leading to complex and time-consuming deployment processes, especially when the platform lacks external network connectivity.
Innovation Solution
A method for configuring an edge computing platform that allows platform-agnostic software development, enabling data scientists to deploy machine learning applications without specific knowledge of the platform, by parsing and customizing software code dependencies for execution, and generating executable software with runtime configuration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If machine learning software is integrated on edge computing platforms, then real-time data processing capability is improved, but deployment complexity increases due to fragmented workflows and technical knowledge gaps
Solution Approach 1:
The patent introduces a configuration manager as an intermediary system that bridges the gap between data scientists and edge computing platforms. This mediator automatically handles the complex deployment process by receiving platform-agnostic code, parsing dependencies, customizing configurations, and generating platform-specific executables, thereby eliminating the need for data scientists to directly manage deployment complexity
Solution Approach 2:
The system enables self-service deployment where the configuration manager automatically performs dependency parsing, customization, and code generation without requiring manual intervention from data scientists. The process is autonomous and handles the entire transformation from platform-agnostic code to platform-specific executable independently
2Ease of operation
If platform-agnostic software development is enabled, then ease of software development is improved, but customization requirements increase for platform-specific execution
Solution Approach 1:
The configuration manager performs preliminary customization actions by automatically parsing dependency data and generating platform-specific configurations before execution. This advance preparation handles the customization requirements transparently, allowing developers to write platform-agnostic code while the system pre-processes all platform-specific adaptations
Solution Approach 2:
The patent replaces manual mechanical customization processes with automated computer-based parsing and code generation. Instead of manually adapting platform-agnostic code for each edge platform, the system uses automated dependency parsing and template-based code generation to create platform-specific executables, substituting manual labor with intelligent automation
3Loss of time
If automated code generation is implemented, then deployment time is reduced, but system complexity increases
Solution Approach 1:
The patent segments the deployment system into distinct functional modules: a code parser for extracting dependency information, a customizer for adapting configurations, and a code generator for producing platform-specific executables. This segmentation allows each component to handle a specific transformation task independently, reducing overall system complexity while enabling automated deployment
Data Source
AI summary
A method for configuring a computing platform for edge computing with a software is provided, which comprises: receiving as input data software code; parsing the software code for identifying in the software code dependency data to be customized for execution of the software on the computing platform; customizing the software code dependency data for execution of the software on the computing platform; generating the software based on the software code and the customized software code dependency data; and loading the software onto the computing platform for execution.