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# Model Selection for Production System via Automated Online Experiments

NIPS 2020, (2020)

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Abstract

A challenge that machine learning practitioners in the industry face is the task of selecting the best model to deploy in production. As a model is often an intermediate component of a production system, online controlled experiments such as A/B tests yield the most reliable estimation of the effectiveness of the whole system, but can o...More

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Introduction

- Evaluating the effect of individual changes to machine learning (ML) systems such as choice of algorithms, features, etc., is the key to growth in many internet services and industrial applications.
- Classical model selection paradigms such as cross-validation consider ML models in isolation and focus on selecting the model with the best predictive power on unseen data.
- This approach does not work well for modern industrial ML systems, as such a system usually consists of many individual components and a ML model is only one of them.

Highlights

- Evaluating the effect of individual changes to machine learning (ML) systems such as choice of algorithms, features, etc., is the key to growth in many internet services and industrial applications
- We propose a new framework of model selection for production system, where the best model is selected via deploying a sequence of models online
- The model selection for production system does not fit into the classical model selection paradigm
- We propose a new approach by taking data collection into the model selection process and selecting the best model via iterative online experiments
- The model to deploy at each iteration is picked by balancing the predicted accumulative metric and the uncertainty of the prediction due to limited data
- With simulated experiments from real data, we show that automated online experimentation (AOE) performs significantly better than all the baselines in terms of identifying the best model and estimating the accumulative metric

Results

- With simulated experiments from real data, the authors show that AOE performs significantly better than all the baselines in terms of identifying the best model and estimating the accumulative metric.

Conclusion

- The model selection for production system does not fit into the classical model selection paradigm.
- The authors propose a new approach by taking data collection into the model selection process and selecting the best model via iterative online experiments.
- It allows selection from a much larger pool of candidates than using A/B testing and gives more accurate selection than off-policy evaluation by actively reducing selection bias.
- With simulated experiments from real data, the authors show that AOE performs significantly better than all the baselines in terms of identifying the best model and estimating the accumulative metric

Summary

## Introduction:

Evaluating the effect of individual changes to machine learning (ML) systems such as choice of algorithms, features, etc., is the key to growth in many internet services and industrial applications.- Classical model selection paradigms such as cross-validation consider ML models in isolation and focus on selecting the model with the best predictive power on unseen data.
- This approach does not work well for modern industrial ML systems, as such a system usually consists of many individual components and a ML model is only one of them.
## Results:

With simulated experiments from real data, the authors show that AOE performs significantly better than all the baselines in terms of identifying the best model and estimating the accumulative metric.## Conclusion:

The model selection for production system does not fit into the classical model selection paradigm.- The authors propose a new approach by taking data collection into the model selection process and selecting the best model via iterative online experiments.
- It allows selection from a much larger pool of candidates than using A/B testing and gives more accurate selection than off-policy evaluation by actively reducing selection bias.
- With simulated experiments from real data, the authors show that AOE performs significantly better than all the baselines in terms of identifying the best model and estimating the accumulative metric

Related work

- Model selection [18] is a classical topic in ML. The standard paradigm of model selection considers a model in insolation and aims at selecting a model that has the best predictive power for unseen data based on an offline dataset. Common techniques such as cross-validation, bootstrapping, Akaike information criterion [AIC, 19] and Bayesian information criterion [BIC, 20] have been widely used

Algorithm 1: model selection with automated online experiments (AOE) Result: Return the ML system with the highest accumulative metric Collect the initial data D0; while Online experiment budget is not over do

Infer p(f |A, X, Dt−1) with VI on surrogate model ; Identify Mt = arg maxMi∈M α(Mi); Deploy Mt and construct Dt by augmenting the collected data into Dt−1 ; end for scoring a model’s predictive power based on a given dataset. As scoring all the candidate models does not scale for complex problems, many recent works focus on tackling the problem of searching a large continuous and/or combinatorial space of model configurations, ranging from hyper-parameter optimization [HPO, 10, 21], automatic statistician [22, 23, 24, 25] to neural network architecture search [NAS, 26]. A more recent work [27] jointly considers the scoring and searching problem for computational efficiency. Online model selection [28, 29] is an extension of the standard model selection paradigm. It still treats a model in isolation but considers the online learning scenario, in which data arrive sequentially and the models are continuously updated. This is different to MSPS, which views a model in the context of a bigger system and actively controls the data collection.

Funding

- With simulated experiments from real data, we show that AOE performs significantly better than all the baselines in terms of identifying the best model and estimating the accumulative metric

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