Edge Software Dependency Configuration for Offline 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, making it difficult to deploy ML-based applications efficiently and securely, especially in environments with no external network connectivity.
Innovation Solution
A method for configuring edge computing platforms that allows platform-agnostic software development, enabling data scientists to create ML applications without specific knowledge of the platform, by parsing and customizing software code dependencies for standalone execution, and supporting seamless integration across disciplines.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If machine learning software is integrated on edge computing platforms using conventional software approaches, then deployment efficiency is reduced due to fragmented workflows and technical knowledge gaps, but platform-specific knowledge requirements increase making deployment more difficult
Solution Approach 1:
The patent introduces a software container as an intermediary layer between the ML application and the edge computing platform. This container includes a runtime environment that abstracts platform-specific details, allowing ML software to be deployed without requiring developers to have platform-specific knowledge. The container acts as a mediator that handles platform compatibility automatically.
Solution Approach 2:
The software container is designed to be universal and platform-agnostic, capable of running on different edge computing platforms without modification. The containerization approach allows the same ML application package to be deployed across multiple platforms, eliminating the need for platform-specific adaptation and reducing deployment complexity.
2Speed
If ML-based applications are deployed on edge computing platforms, then real-time data processing capability is improved, but integration complexity increases due to distinct development and deployment procedures
Solution Approach 1:
The patent segments the ML application deployment into distinct components: the ML model, dependencies, configuration files, and runtime environment, all packaged together in a software container. This segmentation allows each component to be independently managed while maintaining their relationships, simplifying the overall integration process despite the complexity of real-time processing requirements.
Solution Approach 2:
The patent performs preliminary actions by pre-configuring the software container with all necessary runtime environments, dependencies, and configuration files before deployment. This advance preparation eliminates the need for complex post-deployment configuration and reduces integration complexity while maintaining real-time processing capabilities.
3Reliability
If software is customized for specific edge computing platforms, then execution compatibility is improved, but ease of deployment deteriorates as developers need platform-specific knowledge
Solution Approach 1:
The software container serves as an intermediary that ensures execution compatibility across different platforms without requiring developers to customize software for each platform. The containerized runtime environment maintains platform-specific requirements internally while presenting a uniform interface to developers, thus preserving ease of deployment.
Solution Approach 2:
The patent creates a self-contained copy of the necessary runtime environment and dependencies within the software container. This copying approach ensures that the software executes correctly on any platform that can run the container, without requiring platform-specific customization, thereby maintaining both execution compatibility and ease of deployment.
4Reliability
If ML software is deployed in network connectionless environments, then security is improved by isolating from external network attacks, but software update capability is reduced
Solution Approach 1:
The patent performs preliminary actions by including all necessary software updates, patches, and configuration files within the software container before deployment. This allows the isolated edge computing platform to receive updated functionality through container replacement without requiring external network connectivity, thus maintaining both security and adaptability.
Solution Approach 2:
The software container acts as an intermediary that enables update capability in isolated environments. By packaging updates within the container and deploying them as complete units, the system can refresh software capabilities without needing to connect to external networks, preserving security while enabling adaptability.
Data Source
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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.