Making machine learning model deployment boring

The free lunch for machine learning is over. Organizations are quickly ramping up their abilities to automate and professionalize their machine learning processes and infrastructure. As a consequence organizational goals, processes and requirements put an increasing burden on teams to put machine learning models in production. We believe much of this burden relates to engineering issues, which with proper abstractions can be greatly reduced for product teams. In this presentation we will talk about the organizational context of ING and the design our Machine Learning Platform. In the first part we will sketch some organizational context and the requirements it brings. Next, we will picture the kind of use cases and user journey we have in mind. Finally, we will present how these considerations led the platform design we are currently deploying.

This lecture is made possible by bigdata republic.

The slides from this lecture can be viewed here