Automated Experiment Routing for AI Model Testing
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Solution Overview
Problem
Testing iterations of artificial intelligence models is time-consuming and resource-intensive, requiring manual coding and prone to human error, as each iteration necessitates changing code across multiple layers of a model, which is inefficient and error-prone.
Innovation Solution
A system that generates a test model based on an experiment request without requiring code changes at multiple layers, using prefixes and payloads to determine configuration parameters and modify sub-services, allowing for efficient and automated testing of model iterations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual coding is used to change parameter values across multiple model layers, then model iteration testing can be performed, but the process becomes time-consuming and resource-intensive
Solution Approach 1:
The patent introduces a switch configuration system as an intermediary between the test input and model layers. This switch system automatically routes test data to specific model layers and parameter configurations without requiring manual code changes. The switch configuration includes layer identifiers and parameter value mappings that enable automated iteration testing across multiple model layers.
Solution Approach 2:
The patent creates a simplified copy of the model structure in the form of a switch configuration table. This configuration table mirrors the hierarchical structure of the neural network model (layers, sub-layers, parameters) but in a flattened, easily modifiable format. By working with this configuration copy rather than the actual model code, testers can rapidly iterate through different parameter combinations without touching the underlying model implementation.
2Reliability
If manual coding is used for each model iteration, then parameter testing can be performed, but the process becomes prone to human error
Solution Approach 1:
The switch configuration system acts as an error-proof intermediary that automatically translates high-level test specifications into the appropriate model layer configurations. Instead of manually coding complex routing logic across multiple layers, the switch configuration table provides a structured, error-resistant method for specifying parameter variations. The system automatically handles the complexity of mapping test parameters to the correct model layers and sub-layers.
Solution Approach 2:
The system enables self-service testing where the switch configuration automatically manages the complexity of multi-layer parameter testing. The configuration table structure itself provides the routing logic, eliminating the need for separate coding efforts. Each entry in the switch configuration self-describes which layer and parameter it affects, allowing the system to automatically generate the correct test paths without human intervention in the coding process.
3Adaptability or versatility
If code changes are made across multiple layers for each iteration, then comprehensive model testing can be achieved, but the device complexity increases
Solution Approach 1:
The patent segments the complex model configuration into discrete, independently manageable switch entries. Each switch configuration entry represents a single parameter modification at a specific layer, rather than requiring coordinated changes across multiple layers. This segmentation allows testers to modify individual parameters without affecting others, simplifying the testing process while maintaining the ability to test comprehensive model configurations through combination of individual switch entries.
Solution Approach 2:
The switch configuration system provides a universal interface for testing any parameter at any layer of the model. Rather than requiring separate coding paths for different layers and parameters, the same switch configuration mechanism handles all parameter variations uniformly. The configuration table structure is layer-agnostic and parameter-agnostic, providing a single unified approach that works across the entire model hierarchy, thereby reducing overall system complexity.
Data Source
AI summary
This application relates to systems and methods for automatically generating experiments based on experiment requests routed to micro-services (model sub-components) using a prefix-based routing mechanism. In some examples, experiment requests may parsed to determine lower layer services (e.g., components) whose properties need to be changed for a model iteration. Prefixes in requests may be used to route the experiment requests and portions thereof to appropriate services or layers for configuration at the micro-service level. Routing tables at each higher layer may be utilized to determine the correct sub-layers to redirect a request and/or portion thereof. At micro-service level, each micro-service may store and use a configuration table to match a received parameter in a request with a property and its corresponding value for the experiment.