Model explainability, interpretability, and reproducibility
Models are often seen as a black box: data goes in, something happens, and a prediction comes out. This lack of transparency is challenging on a number of levels and is often represented in loosely related terms explainability, interpretability, and reproducibility.
- Explainability: Indicates the description of the internal mechanics of an Machine Learning (ML) model in human terms
- Interpretability: Indicates the ability to:
- Understand the relationship between model inputs, features and outputs
- Predict the response to changes in inputs
- Reproducibility: Indicates the ability to reproduce the output of a model in a consistent fashion for the same inputs
To solve these challenges, CML provides an end-to-end model governance and monitoring workflow that gives organizations increased visibility into their machine learning workflows and aims to eliminate the blackbox nature of most machine learning models.