Stack, production workflow and practice

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In the previous post we talked about why we use Machine Learning at Teads and which particular use cases we work on. In this article we will be covering which technologies we use, why we had to build new solutions and our ML production workflow. We will end with how we actually enhance our ML practice.

ML Stack and why we do not use MLlib

Our stack leverages existing technologies, with Apache Spark sitting at the center. …

4 use cases from the AdTech industry

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Digital Advertising is an astonishing Machine Learning playground, it combines data rich activities, scaling challenges and a lot of automation, especially since the rise of Programmatic buying and selling of ads in real-time.

With 20 billion page views and more than 3 billion unique viewer IDs each month we are now reaching interesting volumes for our algorithms.

In this first post we will describe some of the Machine Learning use cases that we have been working on:

  • View-through rate prediction
  • Broken creative detection
  • Bid-request relevancy prediction
  • Look-alike modeling

View-through rate prediction (VTR)

When we started experimenting machine learning two years ago, we wanted to predict the probability for a video to be watched for more than x seconds, according to the advertiser requirement. This prediction aims at only showing the most interesting ads for the users. Considering that at Teads we charge buyers only when an Ad is viewed, another benefit of using this model is that it avoids the waste of inventory. When the predicted view-through rate for a given advertiser is too low, the display opportunity is free for someone else to take it. …



Lead Data Scientist at Teads

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