Machine Learning at Scale

TL;DR

This paper describes the design and implementation of an automated, scalable machine learning platform that manages billions of advertising impressions and optimizes thousands of campaigns across diverse, real-time digital advertising environments.

Contribution

It introduces a robust, automated platform capable of handling massive data and real-time optimization for digital advertising at scale, demonstrating practical deployment in a complex industry setting.

Findings

Platform processes hundreds of terabytes of data efficiently.

Enables continuous optimization of thousands of campaigns.

Impacts billions of advertising impressions monthly.

Abstract

It takes skill to build a meaningful predictive model even with the abundance of implementations of modern machine learning algorithms and readily available computing resources. Building a model becomes challenging if hundreds of terabytes of data need to be processed to produce the training data set. In a digital advertising technology setting, we are faced with the need to build thousands of such models that predict user behavior and power advertising campaigns in a 24/7 chaotic real-time production environment. As data scientists, we also have to convince other internal departments critical to implementation success, our management, and our customers that our machine learning system works. In this paper, we present the details of the design and implementation of an automated, robust machine learning platform that impacts billions of advertising impressions monthly. This platform enables us to continuously optimize thousands of campaigns over hundreds of millions of users, on multiple continents, against varying performance objectives.