Graphical User Interface for Machine Learning Model Training
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Solution Overview
Problem
Current machine learning model training processes are complex and inaccessible to non-experts, requiring programming expertise and struggling with inconsistent databases and templates, limiting the ability to create, update, and test models effectively.
Innovation Solution
A graphical user interface (GUI) for a model training engine that provides a user-friendly platform for selecting and modifying machine learning model components, executing models with training archives, comparing outputs with desirable features, and modifying coefficients based on differences, while alerting users to compilation errors with graphical explanations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If machine learning algorithms are coded using source code languages, then programming experts can access and modify models, but non-experts cannot use the system and even experts struggle with inconsistent databases and templates
Solution Approach 1:
A graphical user interface (GUI) is introduced as an intermediary between the user and the machine learning model training system. The GUI provides visual components, drag-and-drop functionality, and graphical representations of data flows, allowing users to construct and modify machine learning models without writing source code. This mediator translates user-friendly graphical operations into the underlying programming operations, resolving the contradiction between accessibility for non-experts and functionality for experts.
Solution Approach 2:
The patent replaces the mechanical system of text-based source code programming with a graphical interface system. Instead of manually writing and debugging code, users interact with visual components, icons, and graphical representations of data processing pipelines. This substitution eliminates syntax errors, template inconsistencies, and database connection issues that plague text-based approaches, while maintaining full functionality for complex machine learning operations.
2Ease of operation
If a graphical user interface is provided for non-expert users, then accessibility is improved, but the system must handle both graphical and programming interfaces
Solution Approach 1:
The graphical user interface is designed to serve multiple user types and multiple functions within a single unified system. It provides drag-and-drop model construction for non-experts, visual debugging tools for intermediate users, and programmatic access points for experts. The GUI acts as a universal interface that adapts to different user expertise levels and operational needs, eliminating the need for separate graphical and programming interface systems.
Solution Approach 2:
The system architecture is segmented into distinct functional layers: a presentation layer with the graphical user interface, a processing layer that handles model construction and training, and a data layer that manages databases and archives. This segmentation allows the GUI to remain simple and user-friendly while the underlying complex operations are handled by specialized subsystems, resolving the contradiction between ease of operation and system complexity.
3Manufacturing precision
If model components with computational layers and coefficients are made modifiable, then model performance can be optimized, but the complexity of managing coefficients and attributes increases
Solution Approach 1:
The system implements automated coefficient adjustment and model optimization features that reduce manual intervention. When users modify model components or training parameters, the system automatically recalibrates coefficients, validates configurations, and optimizes performance without requiring users to manually manage each parameter. This self-service capability allows high model accuracy while minimizing the complexity burden on users.
Solution Approach 2:
The graphical interface provides real-time feedback mechanisms that monitor model performance, coefficient values, and training progress. Visual indicators, performance metrics, and automated suggestions are displayed within the GUI, allowing users to make informed adjustments without needing to understand the underlying complexity. This feedback loop enables precise model optimization while keeping the user interface simple and manageable.
Data Source
AI summary
A method for creating one or more machine learning models in a model training engine is provided. The method includes providing to a user, via a graphical user interface, a selection of components for a machine learning model, at least one component having a computational layer including one or more coefficients associated with a component attribute. The method also includes displaying, in the graphical user interface, a component selected by the user, including a selected value of the component attribute and executing the machine learning model with a training archive as an input, to obtain an output indicative of a desired feature of the training archive. The method also includes comparing the output with a desirable feature value, and modifying at least one coefficient in the component of the machine learning model based on a difference between the output from the machine learning model and the desirable feature value.