FEEL THE DIFFERENCE
EXPERT TIME SAVED
Training a neural network includes a huge variety of hyperparameters which differ for each architecture. High uncertainties about the impact of parameters results in time intensive iterative processes. Automate and boost this process with paretos optimization.
Machine learning algorithms themselves have similar to neural nets a huge amount of possible hyperparameters. Boost the training speed and automate the selection of the best hyperparameters.
Models used for predictions are more trustable when uncertainty can be quantification. Reducing the uncertainty by optimization enables to make better predictions with the existing models.
HOW IT WORKS
A.I.1 Optimization2 as a Service3
1 a multi-layer A.I. based algorithm designed for complexity
2 automated mathematical multi-objective optimization
3 easy to connect to the existing tool landscape