  Machines learning techniques plays a preponderant role in dealing with
massive amount of data and are employed in almost every possible domain.
Building a high quality machine learning model to be deployed in production is
a challenging task, from both, the subject matter experts and the machine
learning practitioners.
  For a broader adoption and scalability of machine learning systems, the
construction and configuration of machine learning workflow need to gain in
automation. In the last few years, several techniques have been developed in
this direction, known as autoML.
  In this paper, we present a two-stage optimization process to build data
pipelines and configure machine learning algorithms. First, we study the impact
of data pipelines compared to algorithm configuration in order to show the
importance of data preprocessing over hyperparameter tuning. The second part
presents policies to efficiently allocate search time between data pipeline
construction and algorithm configuration. Those policies are agnostic from the
metaoptimizer. Last, we present a metric to determine if a data pipeline is
specific or independent from the algorithm, enabling fine-grain pipeline
pruning and meta-learning for the coldstart problem.
