Consumers are increasingly expecting retailers to recognize preferences and make recommendations tailored to their needs. A variety of techniques may be employed to accomplish this but each requires careful consideration of how to overcome challenges of scale. In this session, Bryan Smith, Technical Director at Databricks, will explore some foundational approaches to building recommenders that allow organizations of any size to deliver personalized recommendations to their consumers.
Financial Service Institutions (FSI) on average have fewer than 10 machine learning models in production. Banks, Insurers and Asset Managers have arguably seen the least innovation over the last decade compared to other parts of the economy due to regulatory requirements, legacy technologies and long release cycles. In this discussion focused on credit risk analytics, we will demonstrate how a unified data analytics platform brings a more transparent and structured approach to commercial data science in Finance, complying with audit and regulation whilst reducing model lifecycle process from 12 months to a few weeks.