In this webinar

Explainable AI, or XAI, is a rapidly expanding field of research that aims to supply methods for understanding model predictions. Alex will start by providing a general introduction to the field of explainability, introduce the Open Source Alibi library and focus on how it helps you to understand trained models. He will then explore the collection of algorithms provided by Alibi and the types of insight they each provide, looking at a broad range of datasets and models, discussing the pros and cons of each. The aim is to give the ML practitioner a clear idea of how Alibi can be used to justify, explore and enhance their use of ML, especially for models in deployment.

Key points we are going to discuss:

• The importance of explainability.
• How to use Alibi to understand trained models.
• When to use Alibi to enhance your models
• The effectiveness of Seldon Deploy across Serving, Monitoring and Explainability.