Machine Learning Module Network for Algorithm Interoperation
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Solution Overview
Problem
The challenge lies in effectively selecting and executing machine learning algorithms due to the numerous alternatives available, each with varying suitability for specific tasks, and the need for more efficient execution and arrangement of these algorithms to handle complex data sets and deliver accurate results without programmer intervention.
Innovation Solution
A computer-implemented method involving a network of discrete software modules, each comprising a machine learning algorithm, a data store, and a message handler, where the modules communicate through a common interface to refine the approximation of functions, allowing output from one module to serve as input for another, creating a network for combined processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple discrete machine learning algorithms are used to address complex data challenges, then the accuracy and capability of data analysis is improved, but the complexity of selecting and arranging algorithms increases
Solution Approach 1:
The system segments machine learning algorithms into discrete, independently deployable software modules. Each module encapsulates a specific algorithm (e.g., regression, classification, clustering) with standardized interfaces, allowing them to be selected and combined like building blocks. This segmentation reduces the complexity of managing multiple algorithms by providing clear modular boundaries and standardized interaction protocols.
Solution Approach 2:
The framework creates a universal execution environment that can run multiple types of machine learning algorithms through a common interface. The standardized module design allows any algorithm implementing the required interface to be integrated without custom integration code, making the system multi-functional and adaptable to different algorithmic approaches while maintaining consistent deployment procedures.
2Productivity
If machine learning algorithms are tightly coupled to specific tasks, then the performance for that task is optimized, but the adaptability to other tasks decreases
Solution Approach 1:
The system enables dynamic configuration of algorithm networks where modules can be added, removed, or reconfigured based on task requirements. The execution environment dynamically routes data between modules based on the specific task at hand, allowing the same infrastructure to adapt to different machine learning tasks while maintaining task-optimized performance through selective module composition.
Solution Approach 2:
The standardized interface and execution environment act as intermediaries between the algorithm modules and specific tasks. Rather than tightly coupling algorithms to tasks, the framework provides a mediating layer that handles task-specific configuration and data routing, allowing algorithms to remain general-purpose while still achieving task-optimized performance through proper interface implementation.
3Ease of manufacture
If traditional programming techniques are used for data analysis, then the implementation is straightforward, but the capability to handle complex data sets and deliver accurate results is limited
Solution Approach 1:
The system enables machine learning algorithms to be deployed and executed with minimal programmer intervention. The standardized modules come with built-in configuration capabilities and the execution environment automatically handles module instantiation, data routing, and result aggregation. This self-service approach maintains ease of implementation while unlocking the superior accuracy capabilities of machine learning algorithms for complex data analysis.
Data Source
AI summary
A computer implemented method of executing a plurality of discrete software modules each including a machine learning algorithm as an executable software component configurable to approximate a function relating a domain data set to a range data set; a data store; and a message handler as an executable software component arranged to receive input data and communicate output data for the module, wherein the message handler is adapted to determine domain parameters for the algorithm based on the input data and to generate the output data based on a result generated by the algorithm, the method including providing a communication channel between modules in order that at least part of output data for a first module constitutes at least part of input data for a second module so as to create a network of modules for combining machine learning algorithms to refine the approximation of the function.