Hi all,A machine learning pipeline implemented in IGNITE-9158 <https://issues.apache.org/jira/browse/IGNITE-9158> (see discussion here <http://apache-ignite-developers.2346864.n4.nabble.com/ML-Machine-Learning-Pipeline-Improvement-tt32772.html> ) supports hyperparameters variation, but not trainers variation so far.Our reference-framework scikit-learn (according to documentation <http://scikit-learn.org/stable/modules/pipeline.html#pipeline> ) allows to variate trainers and preprocessors the following way:>>> param_grid = dict(reduce_dim=[None, PCA(5), PCA(10)],...                   clf=[SVC(), LogisticRegression()],...                   clf__C=[0.1, 10, 100])>>> grid_search = GridSearchCV(pipe, param_grid=param_grid)I think it would be a great improvement for our ML pipeline.Alexey Zinoviev, it would be awesome if you as an author of original ML pipeline take a look at this proposal.
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