A key challenge in developing and deploying responsible Machine Learning (ML) systems is understanding their performance across a wide range of inputs.
Using WIT, you can test performance in hypothetical situations, analyze the importance of different data features, and visualize model behavior across multiple models and subsets of input data, and for different ML fairness metrics.
Platforms and Integrations
Models served by TF serving
Cloud AI Platform Models
Models that can be wrapped in a python function
Supported data and task types
Tabular, Image, Text data