Sorry I think I explained poorly. Plexe does build deep learning models automatically. When it gets a dataset and a problem description, it automatically evaluates various model architectures (NNs being one of them).

Plexe experiments with multiple approaches - from traditional algorithms like gradient boosting to deep neural networks. It runs the training jobs and compares performance metrics across different architectures to identify which solution best fits your specific data and problem constraints.