Automated Machine Learning Model Pipeline Compiler
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Solution Overview
Problem
The manual maintenance of machine learning models in online systems is time-consuming and error-prone, especially as the number of models increases, leading to reduced accuracy and increased deployment time due to the need for manual modifications and debugging.
Innovation Solution
Machine learning models are constructed in a hierarchical manner, where a parent model serves as a starting point for child models, with a description file specifying dependencies and modifications, allowing a pipeline compiler to automatically generate and update child models based on changes to the parent model, reducing the need for manual intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If engineers manually maintain and modify machine learning models, then model accuracy can be maintained through careful adjustments, but the time and effort required for model maintenance increases significantly as the number of models grows
Solution Approach 1:
The patent segments model maintenance into automated compilation steps versus manual review steps. The pipeline compiler automatically handles configuration generation, dependency resolution, and model updates, while engineers only need to review and approve changes. This segmentation reduces the time engineers spend on routine maintenance tasks while preserving accuracy through selective human oversight.
Solution Approach 2:
The system enables self-service model maintenance through automated pipeline compilation. The pipeline compiler automatically generates configuration files, resolves dependencies between models, and updates models based on parent model changes without requiring manual intervention for each model. This self-service capability significantly reduces maintenance time while maintaining consistency across the model portfolio.
2Adaptability or versatility
If engineers manually modify models to incorporate new features or architecture, then model improvements can be implemented, but the deployment time increases due to manual debugging and verification
Solution Approach 1:
The pipeline compiler performs preliminary actions by automatically generating configuration files and resolving dependencies before model deployment. This pre-compilation process identifies potential issues early and prepares the model update package, reducing the time required for actual deployment and debugging. Engineers can review the prepared changes before deployment, ensuring improvements are implemented correctly.
Solution Approach 2:
The system incorporates feedback mechanisms where the pipeline compiler automatically detects changes in parent models and propagates them to child models. This automated feedback loop ensures that model improvements are consistently applied across the model hierarchy without manual intervention, reducing deployment time while maintaining adaptability through systematic change propagation.
3Adaptability or versatility
If engineers manually maintain each model independently, then model-specific customization can be preserved, but errors are more likely to be introduced and model consistency across the system deteriorates
Solution Approach 1:
The pipeline compiler serves multiple functions: it generates configuration files, resolves dependencies between parent and child models, detects changes automatically, and propagates updates across the model hierarchy. This universal tool maintains model consistency across the system while preserving customization through the dependency-based inheritance mechanism, eliminating the need for independent manual maintenance of each model.
Solution Approach 2:
The patent implements a nested model hierarchy where child models are embedded within parent models through dependency relationships. The pipeline compiler automatically manages this nesting by detecting changes in parent models and propagating them to child models. This nested structure preserves model customization at each level while ensuring consistency across the hierarchy through automated change propagation, preventing errors that would arise from independent manual maintenance.
4Adaptability or versatility
If the system uses a large number of machine learning models to predict various events, then system functionality and prediction coverage are enhanced, but the complexity of model management and maintenance increases greatly
Solution Approach 1:
The pipeline compiler acts as an intermediary between engineers and the large portfolio of machine learning models. It automatically manages configuration generation, dependency resolution, and update propagation across numerous models. This intermediary tool reduces management complexity by handling routine tasks automatically, allowing engineers to focus on high-level decisions while the system maintains a large number of models for comprehensive prediction coverage.
Solution Approach 2:
The system uses template-based configuration copying where child models inherit configurations from parent models through the dependency mechanism. The pipeline compiler automatically copies and adapts configurations across the model hierarchy, reducing the complexity of managing numerous models. This copying approach ensures consistency while allowing customization, enabling the system to maintain a large portfolio of models without proportionally increasing management complexity.
Data Source
AI summary
Machine-trained models are generated based on a model description that defines parameters for training the model and that can inherit parameters from parent model descriptions. When a parent model description changes, the changes made to the parent model description are applied to the model description automatically. When a target model is re-generated, a description of the set of parameters for generating the target model is received. The parent model is then identified from the received description, and a description of the set of parameters for generating the parent model is retrieved. Using the description for the target model and the parent model, a pipeline for generating the target model is generated. Finally, the pipeline is executed to generate the target model.