You’ve collected your data and built your AI models, but how do you scale it across your enterprise? Operationalization of AI is typically an afterthought. To successfully manage AI models, organizations need a consistent, repeatable process for deploying, monitoring, and updating models. Add in integration with critical business applications and there is an increased need for governance and monitoring across AI programs. Join this session to learn how to create efficiencies for data science and engineering teams, and enable a comprehensive monitoring and governance process for all stakeholders through ModelOps. Attendees gain a better understanding and practical examples for establishing and maintaining the right AI pipeline and governance for their organization to include:

·      Discussion of current challenges with moving models from the lab to production environment

·      Importance of a ModelOps pipeline to ease friction between DS & ML engineering teams

·      Enabling governance, monitoring, and risk management

·      Communicating with business stakeholders and the criticality of having transparency, explainability, and trust within your AI process