Reference Guide

Metrics

Metrics are a way to measure the performance of your models or datasets that can then be analyzed in the context of a given problem

• Bayes Error Rate
• Divergence
• Drift Detection
• Outlier Detection
• Sufficiency
• Upperbound Average Precision

Supported Model Backends

The models and model trainers provided by DAML are meant to assist users in setting up architectures that are guaranteed to work with applicable DAML metrics.
Below is a list of backends with available trainers and models.

• PyTorch Models
• Tensorflow Models