  AutoML systems provide a black-box solution to machine learning problems by
selecting the right way of processing features, choosing an algorithm and
tuning the hyperparameters of the entire pipeline. Although these systems
perform well on many datasets, there is still a non-negligible number of
datasets for which the one-shot solution produced by each particular system
would provide sub-par performance. In this paper, we present Amazon SageMaker
Autopilot: a fully managed system providing an automated ML solution that can
be modified when needed. Given a tabular dataset and the target column name,
Autopilot identifies the problem type, analyzes the data and produces a diverse
set of complete ML pipelines including feature preprocessing and ML algorithms,
which are tuned to generate a leaderboard of candidate models. In the scenario
where the performance is not satisfactory, a data scientist is able to view and
edit the proposed ML pipelines in order to infuse their expertise and business
knowledge without having to revert to a fully manual solution. This paper
describes the different components of Autopilot, emphasizing the infrastructure
choices that allow scalability, high quality models, editable ML pipelines,
consumption of artifacts of offline meta-learning, and a convenient integration
with the entire SageMaker suite allowing these trained models to be used in a
production setting.
