Effective Modelling in the Cloud for Insurers and Banks, using Open Source Kubeflow
WHEN: Thursday 24 September 10:00 – 11:30
Building a robust machine learning (ML) software product is radically different from building traditional software. It involves different skill-sets, activities and workflows and hence it needs a whole new generation of supporting tools. Kubeflow is one of the key open source initiatives that address this pressing need and is gaining traction around the globe.
In this webinar we look at the benefits and pitfalls that arise out of building an ML production pipeline or real-time app on an Open Source, Kubernetes-based tool-stack with Kubeflow.
We will compare various strategies and operating models as well as show hands-on demos of our Credo YQ solution based approach.
In this webinar we look at the benefits and pitfalls that arise out of building an ML production pipeline or real-time app on an Open Source, Kubernetes-based tool-stack with Kubeflow.
We will compare various strategies and operating models as well as show hands-on demos of our Credo YQ solution based approach.
This 1.5 hour webinar is offered free of charge and is aimed at anyone involved in developing, deploying or operating quantitative models at financial institutions: data scientists, data and DEVOPS engineers, risk managers, actuaries, ALM or asset management specialists
Presenters
DavidPartner and Senior Risk Modeller
David is a specialist in numerical analysis and parallel computing, with a PhD in computational physics. He has several decades of number crunching experience in industry, academia and the financial sector. Prior to joining Credo in 2015, he worked as a model validator and head of the group-wide risk modelling team of KBC. He currently oversees risk modelling and data science projects at Credo’s customers and contributes to the development of Credo’s software products in these areas.
RadovanData Scientist and YQ product owner
Rado graduated in Financial Economics, specializing in quantitative methods in finance, statistics and time-series econometrics. He is also a machine-learning expert. Before joining CREDO, he worked in financial industry since 2011 as Quant (KBC Group, risk domain) and Data Scientist (CSOB Group, marketing domain).