Data Pipeline Hyperparameter Optimization - Alex Quemy

PyData Warsaw 2018

It is commonly accepted that about 80% of data scientists time is spent on preparing data, including setting up the proper data pipeline or ETL.
For a large part, the proper configuration of a given data pipeline is the result of the data scientist experience and Subject Matter Expert knowledge, plus a dose of arbitrary decisions. What if most of this work could be automated? Better, is it possible to find some universal pipeline configurations that can work well on a wide range of domains and thus transfer what has been learn on one dataset to another?
In this presentation, we show on a PoC that Sequential Model-Based Optimization techniques can be used to tune data pipeline hyperparameters in order to improve model accuracy. We discuss how to measure if optimal configurations are algorithm-specific or independent, shows that, in the specific case of NLP preprocessing operators, there might exist some kind of generally good configurations, independently of the algorithm or the data.
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