  The pipeline optimization problem in machine learning requires simultaneous
optimization of pipeline structures and parameter adaptation of their elements.
Having an elegant way to express these structures can help lessen the
complexity in the management and analysis of their performances together with
the different choices of optimization strategies. With these issues in mind, we
created the AutoMLPipeline (AMLP) toolkit which facilitates the creation and
evaluation of complex machine learning pipeline structures using simple
expressions. We use AMLP to find optimal pipeline signatures, datamine them,
and use these datamined features to speed-up learning and prediction. We
formulated a two-stage pipeline optimization with surrogate modeling in AMLP
which outperforms other AutoML approaches with a 4-hour time budget in less
than 5 minutes of AMLP computation time.
