# ModelPicker

All models that pass ease.ml/ci, in principle, can be deployed. This gives a pool of candidate models at any given time, each of which can be developed under different hypotheses (e.g., different slices of data). While real-world data distribution keeps changing rapidly (e.g., every day), how can we pick the best model, i.e. the one that fits our latest data distribution?

All models that pass ease.ml/ci, in principle, can be deployed. This gives a pool of candidate models at any given time, each of which can be developed under different hypotheses (e.g., different slices of data). While real-world data distribution keeps changing rapidly (e.g., every day), how can we pick the best model, i.e. the one that fits our latest data distribution?

Our view is to enable a rapid MLOps loop: every day (hour), real-world (unlabelled) data comes into the system, and an MLOps engineer would label these fresh data to pick the best model to use for the coming day (hour), from the pool of candidate models. The key technical challenge is to decrease the number of labels required from the MLOps engineer to control the cost of such a process.

In ease.ml/ModelPicker, we designed a novel active learning algorithm which picks the most “informative” data points for an MLOps engineer to label. With our algorithm, we can pick the best out of 102 models over ImageNet, with merely ~1K images to label!

**Input:** (1) A set of models, each of which passed ease.ml/ci; (2) a fresh unlabelled test set.

**Output:** The model with highest test accuracy.

**Action:** The user provides labels on data examples picked by ease.ml/ModelPicker. Deploy the picked model until a new fresh unlabelled test set comes.

## Publications

### 2021

**Online Active Model Selection for Pre-trained Classifiers**

MR Karimi,

**[AISTATS]**International Conference on Artificial Intelligence and Statistics## Abstract

Given pre-trained classifiers and a stream of unlabeled data examples, how can we actively decide when to query a label so that we can distinguish the best model from the rest while making a small number of queries? Answering this question has a profound impact on a range of practical scenarios. In this work, we design an online selective sampling approach that actively selects informative examples to label and outputs the best model with high probability at any round. Our algorithm can also be used for online prediction tasks for both adversarial and stochastic streams. We establish several theoretical guarantees for our algorithm and extensively demonstrate its effectiveness in our experimental studies.