Personalisation at Scale across the BBC
In the BBC Datalab, our mission is to use responsible machine learning to help audience members find the BBC content that is most relevant to them. We currently work across BBC News, Sport, World Service, Sounds, and iPlayer to deliver high quality onward journeys using machine-driven recommendations. As we scale out across the BBC, an important part of this work is reducing the time, effort, and cost required to make the BBC relevant for each audience member. In this talk, I will discuss the tools and framework that BBC Datalab uses for the continuous training and deployment of onward journey recommenders, reflecting on challenges and lessons learned about MLOps in practice.
Anna FitzMaurice is a Senior Data Scineitst at BBC. Previously, Anna was a postdoctoral research fellow on The Alan Turing Institute’s Women in Data Science and AI project. Her research at the Turing sits at the intersection of technology and society, taking a data-driven approach to investigating the systematic exclusion of women from tech, and the impact this is having on the development of AI. As well as industry experience in data science, she holds a PhD from Princeton University in Atmospheric and Oceanic Sciences, with a focus on modelling ice-ocean interactions under future climate change scenarios, and an MMath in Mathematics (first class) from the University of Oxford.