According to Gartner, over 80% of data science projects never make it to production. This is the main problem that enterprises are facing today, when bringing data science into their organization or scaling existing projects. In this session, Senior Data Scientist Nick Brown will share his lessons learned from operationalizing machine learning at IHS Markit. He will discuss the functional requirements required to operationalize machine learning at scale, and what you need to focus on to ensure you have a reliable solution for developing and deploying AI.

We discuss constructing ML pipelines across federated data and computing environments, as well as building production-ready AI applications that work in hybrid environments.