Configurable ML Framework for Reusable Feature Pipelines
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Solution Overview
Problem
The high startup costs and labor-intensive process of creating customized machine learning algorithms result in inefficient use of resources, as existing systems require significant time and computing power for data standardization, labeling, and model training, with each algorithm often being bespoke and non-reusable.
Innovation Solution
A machine learning framework that includes a configuration file defining attributes and transformer applications, enabling automated data standardization, feature identification, and model creation, allowing for rapid development and reuse across similar models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If customized computer programs are created for each machine learning model, then the model can be optimized for specific tasks, but the coding time and labor requirements increase significantly
Solution Approach 1:
The system segments the machine learning pipeline into separate modular components: data extraction modules, transformation modules, and model training modules. Each module can be independently configured and reused, reducing the need to write custom code for each model while maintaining optimization for specific tasks through configurable parameters rather than custom programming.
Solution Approach 2:
The framework provides universal templates and configurations that can be applied across multiple machine learning models. The same extraction and transformation modules can serve different models by adjusting configuration parameters, eliminating the need to create bespoke programs for each model while maintaining task-specific optimization.
2Reliability
If bespoke computer programs are created for each model, then specific task requirements are met, but program reuse becomes impossible
Solution Approach 1:
By dividing the system into independent, configurable modules rather than monolithic custom programs, the framework enables reuse of extraction and transformation components across different models. Each module maintains its functionality through configuration rather than hard-coded logic, allowing adaptation to new tasks without rewriting code.
Solution Approach 2:
The system uses parameter-based configuration to adapt modules to different tasks rather than custom programming. By changing parameters in the configuration files, the same modules can optimize for different tasks while maintaining reusability, resolving the contradiction between task-specific optimization and program reuse.
3Measurement precision
If data standardization and labeling are performed manually for each model, then data quality is ensured, but the process becomes time-consuming and labor-intensive
Solution Approach 1:
The framework performs data standardization and labeling through pre-configured extraction and transformation modules that are set up in advance. These modules automatically process data according to predefined schemas and transformations, eliminating manual standardization work while maintaining data quality through systematic processing. The preliminary configuration of data handling logic enables rapid model development without sacrificing data quality.
4Reliability
If mathematical models are trained on labeled data for each specific task, then optimal performance is achieved, but the time and computing resources required increase
Solution Approach 1:
The framework uses universal data extraction and transformation modules that can serve multiple models simultaneously. By configuring these modules to handle different data types and formats through a common architecture, the system reduces redundant computing operations and resource consumption while maintaining the ability to train models optimized for specific tasks through configurable parameters.
Data Source
AI summary
A system including one or more processors; and one or more non-transitory computer-readable media storing computing instructions that, when executed on the one or more processors, cause the one or more processors to perform operations: receiving, over a computer network, a configuration file to identify feature data for use in a machine learning algorithm; identifying, using the configuration file, the feature data; storing, in an output file on one or more storage devices, the feature data and at least a subset of historical transactions; transmitting, from the one or more storage devices and over the computer network, the output file comprising the feature data for use in a machine learning application; and generating the machine learning algorithm for a machine learning model based on parameters in the output file, wherein the output file is configured to be transferred between at least two machine learning models. Other embodiments are described.