  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.
