Automated Online Experimentation for Model Selection
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Solution Overview
Problem
Machine learning practitioners face challenges in selecting the best model for production deployment due to limitations in existing model selection methods, which often rely on offline evaluations and struggle with complex systems where models interact with uncontrollable factors like user responses, leading to selection bias and inefficiencies in resource usage.
Innovation Solution
The method involves generating a surrogate model based on observations to describe a feedback distribution for a given context and action, scoring candidate models using an acquisition function, and iteratively refining the model selection process through online experimentation to balance exploration and exploitation, allowing for the deployment of the best model from a large pool of candidates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If offline evaluation methods (cross-validation, bootstrapping, AIC, BIC) are used to score models, then model predictive power can be estimated, but the methods do not account for uncontrollable factors like user responses and produce selection bias
Solution Approach 1:
The patent implements feedback loops where online experiment results (user responses, system metrics) are continuously fed back to update the surrogate model and refine model scoring. This closed-loop feedback mechanism allows the system to learn from actual production performance and adjust selections accordingly, resolving the disconnect between offline predictions and online reality.
Solution Approach 2:
The patent introduces a surrogate model as an intermediary between candidate models and production deployment. This surrogate model simulates production environment behavior and user responses, allowing unbiased evaluation of models without directly exposing them to real users during the selection phase, thereby eliminating selection bias while maintaining evaluation accuracy.
2Reliability
If online controlled experiments (A/B tests) are used to evaluate models, then reliable estimation of system effectiveness is obtained, but only a few models can be compared due to budget constraints
Solution Approach 1:
The patent creates a virtual copy of the production environment through the surrogate model, which simulates user responses and system metrics. This copying approach allows numerous candidate models to be evaluated in parallel within the simulated environment without consuming actual experiment budgets, enabling comprehensive model comparison while maintaining reliable effectiveness estimation.
Solution Approach 2:
The patent performs preliminary evaluation of many candidate models through the surrogate model before committing to actual online experiments. This preliminary action filters out poor-performing models early, allowing limited experiment budgets to be focused on comparing only the most promising candidates, thereby increasing both the number of models initially considered and the reliability of final selections.
3Measurement precision
If standard model selection methods treat models in isolation, then predictive power can be optimized, but the context of the bigger production system is not considered
Solution Approach 1:
The patent merges model evaluation with production system evaluation by designing the surrogate model to simulate the entire system context including user behaviors, data pipelines, and performance metrics. This combining approach ensures that models are scored based on their actual impact on system-wide objectives rather than isolated predictive performance, achieving both precision and adaptability.
4Measurement precision
If all candidate models are scored using standard techniques, then comprehensive evaluation is achieved, but the process does not scale for complex problems with large configuration spaces
Solution Approach 1:
The patent segments the model selection process into multiple stages: initial surrogate model evaluation, acquisition function scoring, and targeted online experimentation. This segmentation divides the complex evaluation task into manageable portions, allowing comprehensive assessment of large model configuration spaces while maintaining computational scalability at each stage.
Solution Approach 2:
The patent applies partial action by using the surrogate model to evaluate all candidate models and then applying excessive action through the acquisition function to identify and focus resources on the most promising subset. This two-tier approach ensures comprehensive initial evaluation while enabling scalable refinement, avoiding the need to exhaustively evaluate every model with expensive online experiments.
Data Source
AI summary
Disclosed examples include an automated online experimentation mechanism that can perform model selection from a large pool of models with a relatively small number of online experiments. The probability distribution of the metric of interest that contains the model uncertainty is derived from a Bayesian surrogate model trained using historical logs. Disclosed techniques can be applied to identify a superior model by sequentially selecting and deploying a list of models from the candidate set that balance exploration-exploitation.