We will discuss a little about what it means to develop AI in a transparent way. We will introduce our interpretability toolkit which enables you to use different state-of-the-art interpretability methods to explain your models decisions. By using this toolkit during the training phase of the AI development cycle, you can use interpretability output of a model to verify hypotheses and build trust with stakeholders. You can also use the insights for debugging, validating model behavior, and to check for bias. You can also use this toolkit at inferencing time to explain the predictions of a deployed model to the end users.

The containers can then be deployed to IoT Edge devices. In this session, we provide a scenario about the importance of edge intelligence, and an overview of Azure ML and Azure IoT Edge. Then we demonstrate how to use Azure ML to create a model and run it on an IoT Edge device.