Predictive Model Importation With User-Specific Resource Selection
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Solution Overview
Problem
Existing predictive modeling systems face challenges in efficiently selecting and executing model implementations based on user-specific factors and available resources, leading to suboptimal performance and resource utilization.
Innovation Solution
A system that selects predictive model implementations based on user-specific properties such as resource consumption, time of day, and account balance, and executes them in a directed graph order, allowing for parallel execution and efficient resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple model implementations are maintained for different user scenarios, then prediction accuracy and user-specific optimization are improved, but system complexity and resource management overhead increase
Solution Approach 1:
The system segments model implementations into multiple versions tailored to different user scenarios, allowing each segment to be optimized for specific user needs while maintaining overall system functionality through modular architecture
Solution Approach 2:
The system changes parameters such as model size, computation depth, and resource allocation based on user-specific factors including historical resource consumption patterns, time of day, and account balance, enabling dynamic optimization without requiring completely different model architectures
2Measurement precision
If larger model implementations are used to improve prediction performance, then prediction accuracy is improved, but resource consumption and cost increase
Solution Approach 1:
The system dynamically selects model implementation size and complexity based on real-time user context including historical resource consumption, current time of day, and account balance, allowing resource usage to adapt to user capacity rather than using fixed large models for all users
Solution Approach 2:
The system changes model parameters such as computation depth, model size, and parallelization level based on user-specific factors, enabling the same model family to be executed at different resource intensities to match user resource availability
3Reliability
If model execution order is strictly prescribed by graph structure, then execution reliability and consistency are improved, but parallelization opportunities and execution speed are reduced
Solution Approach 1:
The system segments the model execution graph into independent executable units that can be processed in parallel when dependencies allow, while maintaining strict ordering constraints for paths where consistency is required, achieving both parallelization and reliability
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for obtaining a plurality of model representations of predictive models, each model representation associated with a respective user and expresses a respective predictive model, and selecting a model implementation for each of the model representations based on one or more system usage properties associated with the user associated with the corresponding model representation.