Sample, Estimate, Tune: Scaling Bayesian Auto-Tuning of Data Science Pipelines

In this paper, we describe a system for sequential hyperparameter optimization that scales to work with complex pipelines and large datasets. Currently, the state-of-the-art in hyperparameter optimization improves on randomized and grid search by using sequential Bayesian optimization to explore the space of hyperparameters in a more informed way. These methods, however, are not scalable, as the entire data science pipeline still must be evaluated on all the data. By designing a sub sampling based approach to estimate pipeline performance, along with a distributed evaluation system, we provide a scalable solution, which we illustrate using complex image and text data pipelines. For three pipelines, we show that we are able to gain similar performance improvements, but by computing on substantially less data.