  The effectiveness of the machine learning methods for real-world tasks
depends on the proper structure of the modeling pipeline. The proposed approach
is aimed to automate the design of composite machine learning pipelines, which
is equivalent to computation workflows that consist of models and data
operations. The approach combines key ideas of both automated machine learning
and workflow management systems. It designs the pipelines with a customizable
graph-based structure, analyzes the obtained results, and reproduces them. The
evolutionary approach is used for the flexible identification of pipeline
structure. The additional algorithms for sensitivity analysis, atomization, and
hyperparameter tuning are implemented to improve the effectiveness of the
approach. Also, the software implementation on this approach is presented as an
open-source framework. The set of experiments is conducted for the different
datasets and tasks (classification, regression, time series forecasting). The
obtained results confirm the correctness and effectiveness of the proposed
approach in the comparison with the state-of-the-art competitors and baseline
solutions.
