Model Integration Tool for Debugging Predictive Analytics
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Solution Overview
Problem
Existing software development tools face inefficiencies in deploying and debugging analytical or predictive models, as they often require distinguishing between platform-specific coding errors and errors in modeling logic, leading to inefficient use of computing resources.
Innovation Solution
A model integration tool that facilitates building, testing, and debugging predictive or analytical models by generating PMML code or other suitable modeling code, which can be executed without creating platform-specific source code, allowing for efficient identification and correction of modeling logic errors before deployment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If program code for implementing a model is deployed and executed on a data-processing platform, then the model can be evaluated in a production environment, but computing resources (network bandwidth, processing cycles, memory) are consumed that may be otherwise deployed in the data-processing environment
Solution Approach 1:
The patent segments the model evaluation process into two distinct phases: (1) platform-agnostic model logic evaluation using PMML in a development environment, and (2) platform-specific code deployment to production systems. This segmentation allows model evaluation to occur without consuming production computing resources, while still ensuring model correctness through automated testing and validation in the development environment.
Solution Approach 2:
The patent implements preliminary action by performing comprehensive model evaluation, testing, and validation in the development environment before deploying code to production. The PMML-based evaluation framework预先 (pre-) identifies modeling logic errors, validates model behavior, and generates test cases, so that when code is deployed to production, only platform-specific integration issues remain, minimizing the need for production resource consumption for debugging.
2Reliability
If program code for implementing a model is executed in a production environment, then the efficacy of the model can be evaluated, but the debugging process must distinguish errors specific to the software platform from errors in the modeling logic
Solution Approach 1:
The patent segments error types into two distinct categories handled in different environments: modeling logic errors are detected and corrected in the development environment using PMML-based validation, while platform-specific errors are isolated to the deployment phase. This segmentation eliminates the need to distinguish between error types during debugging, as each environment is responsible for detecting specific error categories.
Solution Approach 2:
The patent introduces PMML (Predictive Model Markup Language) as an intermediary representation that bridges model development and platform deployment. The PMML format serves as a platform-agnostic intermediate representation that allows model logic to be validated, tested, and evaluated without being tied to any specific software platform, thereby simplifying the debugging process by separating model logic verification from platform-specific implementation.
3Productivity
If platform-specific source code is generated and deployed for model implementation, then the model can be executed on the target platform, but errors in modeling logic may be difficult to identify and correct
Solution Approach 1:
The patent performs preliminary validation and testing of model logic using PMML in the development environment before generating platform-specific code. Automated evaluation frameworks validate model behavior, test edge cases, and verify logical correctness against expected outcomes, ensuring that modeling logic errors are detected and corrected before code generation and deployment, thereby preventing logic errors from reaching production.
Solution Approach 2:
The patent creates and validates a PMML-based copy or representation of the model in a platform-agnostic environment before generating platform-specific code. This PMML copy serves as a verified reference implementation that can be evaluated, tested, and validated independently, allowing model logic to be thoroughly checked before the actual platform-specific deployment occurs.
Data Source
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AI summary
Certain aspects involve building and debugging models for generating source code executed on data-processing platforms. A target data-processing platform is identified that requires bin ranges for modeling coefficients and reason codes for input attributes. A processor outputs source code, which is generated from a modeling code, in a programming language used by the target data-processing platform.