Towards Interactive Curation & Automatic Tuning of ML Pipelines

Abstract

Democratizing Data Science requires a fundamental rethinking of the way data analytics and model discovery is done. Available tools for analyzing massive data sets and curating machine learning models are limited in a number of fundamental ways. First, existing tools require well-trained data scientists to select the appropriate techniques to build models and to evaluate their outcomes. Second, existing tools require heavy data preparation steps and are often too slow to give interactive feedback to domain experts in the model building process, severely limiting the possible interactions. Third, current tools do not provide adequate analysis of statistical risk factors in the model development. In this work, we present the first iteration of QuIC-M (pronounced quick-m), an interactive human-in-the-loop data exploration and model building suite. The goal is to enable domain experts to build the machine learning pipelines an order of magnitude faster than machine learning experts while having model qualities comparable to expert solutions.

Original document

The different versions of the original document can be found in:

• http://dl.acm.org/ft_gateway.cfm?id=3209891&type=pdf

• http://dl.acm.org/ft_gateway.cfm?id=3209891&ftid=1966746&dwn=1,

http://dx.doi.org/10.1145/3209889.3209891 under the license http://www.acm.org/publications/policies/copyright_policy#Background

• https://dblp.uni-trier.de/db/conf/sigmod/deem2018.html#BinnigBCCKSUZZ18,

https://dl.acm.org/citation.cfm?id=3209891,

https://dl.acm.org/citation.cfm?doid=3209889.3209891,

http://tubiblio.ulb.tu-darmstadt.de/105228,

https://doi.org/10.1145/3209889.3209891,

https://academic.microsoft.com/#/detail/2804598380