Interpretable Automated Machine Learning in Maana(TM) Knowledge Platform
This addresses the problem of limited industry adoption of machine learning due to interpretability issues, though it appears incremental as an extension of existing automated machine learning services.
The paper tackles the lack of interpretability in automated machine learning for industrial applications by introducing Maana Meta-learning Service, which performs pipeline search and hyper-parameter tuning while generating structured knowledge for decisions, validated on benchmark datasets.
Machine learning is becoming an essential part of developing solutions for many industrial applications, but the lack of interpretability hinders wide industry adoption to rapidly build, test, deploy and validate machine learning models, in the sense that the insight of developing machine learning solutions are not structurally encoded, justified and transferred. In this paper we describe Maana Meta-learning Service, an interpretable and interactive automated machine learning service residing in Maana Knowledge Platform that performs machine-guided, user assisted pipeline search and hyper-parameter tuning and generates structured knowledge about decisions for pipeline profiling and selection. The service is shipped with Maana Knowledge Platform and is validated using benchmark dataset. Furthermore, its capability of deriving knowledge from pipeline search facilitates various inference tasks and transferring to similar data science projects.