A practical example of enabling Machine Learning functions in your enterprise

With the AI hype cycle still being pretty active, a lot of companies are still struggling with how best to bootstrap and more importantly successfully point their AI capabilities to achieve bottom line growth. Having spent the last a few years bootstrapping Machine Learning at Workday, we have learnt a lot about how best to take a Machine Learning function to solve real business problems. With investments in Machine Learning infrastructure and environments, building distributed Machine Learning microservices that are part of cohesive enterprise SaaS infrastructure and by establishing and streamlining Data Science use case pipeline processes, we have gone from being able to ship 1 product use case a year to productizing 3-5 use cases every 6 months. In the session, we will share a deep learning use case in Workday Expense application to demonstrate how the thought process works practically and how we deliver the solution with the scale and performance to meet enterprise customer requirements. You will walk out of this session with a practical framework that will help you take the Machine learning function successfully in your company.