Model robustness is a large term that encompasses many characteristics that predict how well a model will perform in real-life scenarios. Typically, when training a ML model, data scientists focus on improving a single metric: accuracy.
Accuracy measures how well the model can accurately perform the right prediction given the training and validation sets. However, this leaves the model vulnerable to a host of other shortcomings.
Our machine learning model validation tool seeks to address these shortcomings by giving visibility to more than just accuracy and allow business stakeholders to deploy ML models into production with confidence.