To ensure the continued benefit of a machine learning project, our work doesn’t stop after model deployment. With an ever-changing world and continued increase of decisions being driven by ML, it becomes increasingly important to understand any changes to the data and the model. We need to develop sophisticated monitoring to ensure expected performance of the model, highlight abnormalities, as well as provide meaningful set of result for the end user to act upon.
The concept of monitoring is well known, often the requirement is an open-ended question. This talk will highlight some of the important points to consider when developing your ML monitoring framework.
Dapeng Wang is a Senior Data Scientist at the insurance company LV=. He graduated in maths from the University of Cambridge and has an MSc from the University of Sussex. At LV=, Dapeng is leading in the adoption of Deep Learning across the company. He is currently developing the end to end pipeline to build and integrate Deep Learning within current LV= processes. Dapeng is also a frequent Kaggle competitor and Kaggle competition expert. Dapeng looks forward to using his experience to help the deep learning community find suitable and better implementation solutions for deep learning.