Data

Without standards, the governance of artificial intelligence is a central but emerging issue, enabling compliance that promotes interoperability, security, or reliability. It introduces better coordination of actions, bringing Data and AI teams closer together to federate their work with common best practices. 

This training aims to present a reference architecture to associate the value chain of technology with that of intelligent information on dimensions as diverse and cross-cutting as security, ethics, and infrastructure. A classification to define what needs to be supervised, how to maintain control and compliance on AI as well as the types of AI that are available for different technologies, industries, and standards. A review of more technical models such as MLOps, a presentation of how technical governance of Artificial Intelligence is executed within the main platforms (AzureML).