Speaker: William Nelson, PhD
Linkedin: https://www.linkedin.com/in/william-nelson-wtn20/

Topic: Scalable real time model deployment

Schedule:
6:15pm - 6:45pm -Pizza & Drinks
6:45pm - 7:35pm - Talk
7:35pm - 8:15pm - Q&A & Networking

Bio:
After completing a PhD in theoretical physics at King's College London, William worked at Penn State University as a research scientist focusing on computational & analytical models of physical phenomena, mathematical theories of gravitational wave propagation, & probabilities within quantum cosmological models. He continued as a research scientist at the Nijmegen University in the Netherlands where he received a Marie Curie Fellowship to continue his research on the theories of quantum cosmology. After his years of research, William joined Princeton Consultants as a systems integration specialist at a major transportation company, focusing on various networking & data platforms, notably around Positive Train Control. Since 2016, William has been a Data Scientist at Intent Media, working to build & improve the core ML models that predict on-site user behavior. These models are used to make realtime decisions about user experience on most of the worlds largest travel sites.

Abstract:
At Intent Media we make real time decisions about how to treat users based on machine learning models. These models are trained daily on huge historical datasets, use real time data & return predictions in tens of milliseconds.

The real challenge is that we have hundreds of models, each of which have their own configurations, defaults, accuracy thresholds, decision strategy etc.

I will describe how we coordinate these models, how we automate the model publishing pipeline & how we monitor everything to ensure the best model is used every time.

I will keep the presentation at a reasonably high level, so anyone with experience (or interest) in product-ionizing machine learning models at scale should be able to take away something useful. No special background is required.

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