Configuration-Driven ML Framework for Reusable Model Creation
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Solution Overview
Problem
The high startup costs and labor-intensive process of creating customized machine learning algorithms, including data standardization, labeling, and model training, result in inefficient use of resources and time, as existing systems lack a framework for reusing code across similar models.
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 of machine learning models across different applications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If customized machine learning algorithms are created for each specific task, then model accuracy and performance are improved, but development time and resource costs increase significantly
Solution Approach 1:
The patent implements a universal machine learning framework that can handle multiple different machine learning tasks through a single standardized system. The framework provides common infrastructure for data preprocessing, feature engineering, model training, and evaluation that can be applied across various algorithms and use cases, eliminating the need to build separate customized systems for each task while maintaining task-specific performance requirements
Solution Approach 2:
The patent divides the machine learning development process into distinct modular components including data ingestion modules, preprocessing modules, feature engineering modules, model training modules, and evaluation modules. Each module can be independently developed, tested, and reused across different projects, reducing overall development time while maintaining the ability to create task-optimized models
2Manufacturing precision
If data standardization and labeling are performed manually for each algorithm, then data quality is improved, but labor costs and processing time increase
Solution Approach 1:
The patent implements automated data preprocessing pipelines that perform standardization, cleaning, and validation operations without requiring manual intervention for each dataset. The system automatically detects data types, applies appropriate transformations, and validates data quality standards, enabling rapid processing of large volumes of data while maintaining consistent quality across different projects
Solution Approach 2:
The patent establishes standardized data preprocessing templates and configuration files that define data quality requirements, transformation rules, and validation criteria in advance. These pre-configured templates can be applied to new datasets automatically, ensuring consistent data quality standards are met without requiring manual setup for each new project
3Reliability
If bespoke computer programs are created for each machine learning task, then task-specific optimization is improved, but code reusability and efficiency decrease
Solution Approach 1:
The patent creates a universal machine learning framework with standardized interfaces and configuration mechanisms that can accommodate task-specific requirements through configurable parameters rather than custom code. The framework provides common functionality for data loading, preprocessing, model training, and evaluation that can be applied across different machine learning tasks while maintaining the ability to optimize for specific tasks through configuration
4Reliability
If manual model creation processes are used, then model accuracy can be optimized, but resource consumption and time requirements increase
Solution Approach 1:
The patent implements automated model evaluation and selection mechanisms that use performance metrics to guide the model creation process. The system automatically trains multiple candidate models, evaluates them against validation data, and selects the best-performing model based on predefined accuracy criteria. This feedback-driven approach maintains model accuracy optimization while reducing the time required through automation
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.