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Real Time Bidding Models in Computational Advertising

By Allie


This talk was presented at PyBay2019 - 4th annual Bay Area Regional Python conference. See for more details about PyBay and click SHOW MORE for more information about this talk.

Description The talk provides an overview of the ad tech ecosystem, in particular how ad slots are dynamically populated by way of user behavioral targeting each time a webpage loads. It should be interesting for developers as well as data scientists who are looking to learn about the applications of machine learning in computational advertising.

Abstract In particular, we will explore how DSPs (demand side platforms) optimize ad campaigns for advertisers.

The focus will be on how CTR (click-through-rates) are optimized via real-time-bidding (RTB) models. We will discuss modeling challenges such as how best to handle categorical or imbalanced training data. You will come away with a deeper understanding of the system design trade-offs and be able to deploy models at scale in your own work.

About the speaker Allie is a data scientist at, a fintech company based in Oakland whose mission is to end the paycheck-to-paycheck cycle.

Prior to Even, Allie worked as an engineer and first data scientist at early-stage startups in the consulting, sales tech, and ad tech space in the Bay area.

Sponsor Acknowledgement This and other PyBay2019 videos are via the help of our media partner AlphaVoice (!

#pybay #pybay2019 #python #python3


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