Our team uses Hyperparameter Optimization (HPO), which is a mechanism for automatically exploring a search space of potential Hyperparameters, building a series of models and comparing the models using metrics of interest. To use HPO we work with your business to specify ranges of values to explore for each Hyperparameter.

Experts from our teams help you in determining the parameters of your Neural-Networks effectively and log metrics. This is a challenging problem due to the extremely large configuration space (for instance: how many nodes per layer, activation functions, learning rates, drop-out rates, filter sizes, etc.) and the computational cost of evaluating a proposed configuration (e.g., evaluating a single configuration can take hours to days).

Parametric models allow your business to find the best combination of parameters to input into a Machine Learning algorithm. This makes the models simpler, faster and less reliant on training data. Helping you get up and running quickly.