Configurable Machine Learning Systems via Graphical Interfaces
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Solution Overview
Problem
Current machine learning systems face challenges in scalability, resource efficiency, and usability due to the need for multiple specialized systems for different tasks, leading to increased storage and processing demands, and requiring programming expertise for customization.
Innovation Solution
A system where a central server offloads the building, training, and running of machine learning systems to separate slave servers, allowing for parallel processing with different datasets and configurations, and provides a graphical user interface for non-experts to select and configure machine learning systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple specialized machine learning systems are stored for different tasks, then task-specific accuracy is improved, but storage requirements and resource consumption increase
Solution Approach 1:
The patent implements a universal machine learning system that can perform multiple different tasks through dynamic configuration rather than storing separate specialized systems. The system uses a single neural network architecture that can be adapted to different classification tasks by modifying parameters, training data, and configuration settings, thereby achieving task-specific accuracy without the storage overhead of multiple dedicated systems.
Solution Approach 2:
The system employs dynamic configuration capabilities where the machine learning system can be reconfigured at runtime to handle different tasks. This includes dynamically adjusting parameters, selecting different training datasets, and modifying system behavior based on the specific task requirements, allowing one system to replace multiple static specialized systems.
2Quantity of substance
If a single general machine learning system is stored, then storage efficiency is improved, but task-specific accuracy deteriorates
Solution Approach 1:
The patent creates a universal machine learning system designed to handle multiple task types within a single architecture. By incorporating configurable parameters and the ability to load different training datasets, the system maintains storage efficiency while preserving task-specific accuracy through adaptive configuration rather than through task-specific hardcoding.
Solution Approach 2:
The system achieves task-specific performance by dynamically changing parameters such as training data selection, hyperparameters, and configuration settings rather than changing the underlying system architecture. This allows a single stored system to adapt to different tasks with high accuracy while maintaining storage efficiency.
3Measurement precision
If machine learning systems are customized for specific problems, then accuracy is improved, but ease of operation deteriorates due to programming expertise requirements
Solution Approach 1:
The patent implements automated system configuration capabilities that reduce the need for manual programming expertise. The system can automatically select appropriate parameters, configure training processes, and adapt to different tasks based on the input data and task requirements, making it easier for non-experts to operate while maintaining high accuracy through intelligent self-configuration.
Solution Approach 2:
The system incorporates pre-configured templates, default parameter settings, and pre-trained models that can be directly applied to common tasks. This preliminary preparation allows users to quickly deploy accurate machine learning systems without extensive programming or configuration work, bridging the gap between accuracy and ease of operation.
4Productivity
If multiple machine learning systems run simultaneously, then productivity is improved, but resource consumption increases
Solution Approach 1:
The patent implements a modular architecture where the machine learning system can be divided into independent components that can be selectively activated. Different task modules can be loaded and executed in parallel when needed, allowing the system to scale resource usage according to actual demand rather than maintaining all capabilities continuously active, thus improving productivity while controlling resource consumption.
Data Source
AI summary
Systems and methods for presenting configurable machine learning systems through graphical user interfaces are disclosed. In an embodiment, a machine learning server computer stores one or more machine learning configuration files. A particular machine learning configuration file of the one or more machine learning configuration files comprises instructions for configuring a machine learning system of a particular machine learning type with one or more first machine learning parameters. The machine learning server computer displays through a graphical user interface, a plurality of selectable parameter options, each of which defining a value for a machine learning parameter. The machine learning server computer receives a particular input dataset. The machine learning server computer additionally receives, through the graphical user interface, a selection of one or more selectable parameter options corresponding to one or more second machine learning parameters different from the one or more first machine learning parameters. The machine learning server computer replaces in the particular machine learning configuration file, the one or more first machine learning parameters with the one or more second machine learning parameters. Using the particular machine learning configuration file, the machine learning server computer configures a particular machine learning system. Using the particular machine learning system and the particular input dataset, the machine learning server computer computes a particular output dataset.