AI News, Machine Learning @ Teads (part 2)

Machine Learning @ Teads (part 2)

Our training jobs are scheduled by Jenkins, their dependencies are handled by Coursier, and they run on AWS Elastic Map Reduce with training logs and resulting models stored on S3.

Our specific use cases require to be able to work with large streams of ad delivery events such as bid requests, impressions or complete views and continuously update our models.

The runtime is still based on Spark and our library acts as an abstraction layer between Spark and underlying implementations from Breeze and embeds our custom algorithms.

This library is part of a more general prediction framework that enables to test new experimental approaches and guarantees that the same code is used both online and offline.

The generated application logs are then used, together with other sources of data (DMPs, etc.) to build training sets [2].

For the training jobs [3], these data sets are randomly split into several partitions that are balanced to avoid hotspots.

A/B tests aim at deciding which algorithm, which home page, which user interface, etc., provides the best results in terms of relevant criteria such as traffic and revenue.

We usually split the population into three and define one small population to be able to identify if there is a major issue with the experiment.

This validation is made by iterations to be as close as possible to the production cycle, as illustrated in the following chart (3 iterations).

Thus, we focus on the MSE of the expected revenue (Cost per View or CPV) and not the MSE of a billable probability: We created a tool, called Datakinator, to facilitate the creation of homogeneous experiments that all respect the same testing protocol.

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