Cross-Device Neural Network Node-Graph Pipelines for No-Code Deployment
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Solution Overview
Problem
The challenge lies in efficiently comparing and deploying machine learning models across different devices and platforms without the need for extensive coding, particularly in bridging the gap between model development and deployment, and facilitating real-time testing and debugging.
Innovation Solution
A no-code machine learning graph building platform, RAPSAI, enables users to interactively build and prototype machine learning models using an intuitive graphical user interface, allowing users to drag and drop inputs, models, and outputs, and visualize results in real-time, without writing code.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If traditional machine learning model deployment methods are used, then model deployment is achieved, but extensive coding and complex development processes are required
Solution Approach 1:
The patent replaces traditional code-based model deployment mechanisms with a visual node-graph interface. Users can deploy machine learning models by visually connecting nodes representing different ML components through a graphical user interface, eliminating the need for extensive coding and simplifying the deployment process while maintaining full functionality.
2Adaptability or versatility
If cross-device model deployment is implemented, then model versatility across platforms is improved, but testing and debugging complexity increases
Solution Approach 1:
The patent creates a universal node-graph platform that can deploy machine learning models across multiple device types and platforms through a single visual interface. The system abstracts device-specific complexities behind a unified graphical workflow, allowing the same model pipeline to be deployed to different devices without increasing testing or debugging complexity.
3Productivity
If rapid prototyping is enabled through no-code interface, then development time is reduced, but model customization capability may be limited
Solution Approach 1:
The patent segments machine learning model construction into discrete, visually representable nodes that can be independently configured and connected. Each node represents a specific ML component or operation, allowing users to rapidly assemble models through visual connections while maintaining full customization capability by selecting and configuring individual nodes according to specific model requirements.
Data Source
AI summary
A method includes providing an interactive graphical user interface comprising a first menu providing one or more input options, a second menu providing one or more machine learning models, and a third menu providing one or more output formats. The method also includes generating a graph in a portion of the interactive graphical user interface by detecting one or more user selections of an input option, a machine learning model, and an output format, displaying nodes corresponding to the input option, the machine learning model, the output format, and displaying edges connecting the first node to the second node, and the second node to the third node. The method additionally includes applying the machine learning model to an input associated with the input option to generate an output in the output format. The method further includes providing, by the interactive graphical user interface, the output in the output format.