Introducing Manifold – Towards Data Science

Machine learning programs defer from traditional software applications in the sense that their structure is constantly changing and evolving as the model builds more knowledge. As a result, debugging and interpreting machine learning models is one of the most challenging aspects of real world artificial intelligence(AI) solutions. Debugging, interpretation and diagnosis are active areas of focus of organizations building machine learning solutions at scale. Recently, Uber unveiled Manifold, a framework that utilizes visual analysis techniques to support interpretation, debugging, and comparison of machine learning models. Manifold brings together some very advanced innovations in the areas of machine learning interpretability to address some of the fundamental challenges of visually debugging machine learning models.