  This paper proposes a knowledge-driven AutoML architecture for pipeline and
deep feature synthesis. The main goal is to render the AutoML process
explainable and to leverage domain knowledge in the synthesis of pipelines and
features. The architecture explores several novel ideas: first, the
construction of pipelines and deep features is approached in an unified way.
Next, synthesis is driven by a shared knowledge system, interactively queried
as to what pipeline operations to use or features to compute. Lastly, the
synthesis processes takes decisions at runtime using partial solutions and
results of their application on data. Two experiments are conducted to
demonstrate the functionality of a na\"{\i}ve implementation of the proposed
architecture and to discuss its advantages, trade-offs as well as future
potential for AutoML.
