Building effective machine learning (ML) systems means asking a lot of questions. It’s not enough to train a model and walk away. Instead, good practitioners act as detectives, probing to understand their model better: How would changes to a datapoint affect my model’s prediction? Does it perform differently for various groups–for example, historically marginalized people? How diverse is the dataset I am testing my model on?
To help to address these questions Google launched the What-If Tool, a new feature of the open-source TensorBoard web application, which let users analyze and better understand an ML model without writing code.