Extreme-scale Ad-Tech using Spark and Databricks at MediaMath

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MediaMath is a leading ad-tech platform that responds to over 200 billion ad-opportunities daily, and leverages massive amounts of data to power smarter digital marketing. They use Spark heavily both in production and R&D to develop innovative, proprietary, and scalable solutions to multiple problems: (a) machine-learning models for predicting conversion probability given an ad-impression; (b) measuring causal effectiveness of advertising in randomized tests; (c) running simulations to understand the impact of cookie refreshes and other phenomena on ad effectiveness metrics; (d) finding deviceIDs belonging to the same user based on possibly noisy external deterministic information. In this presentation Prasad will describe these problems briefly, and dive deeper into how MediaMath extensively uses Spark and the Databricks platform to evaluate multiple machine learning models and model-update and calibration schemes, and visualize results.

About Prasad Chalasani

Prasad Chalasani is the SVP of Data Science at Media Math, leading the development of innovative, proprietary scalable algorithms, and analytics that leverage massive amounts of data to power smarter digital marketing for the world’s leading advertisers. Prior to joining Media Math, Prasad led Data Science at Yahoo Research, and before that worked for 10 years as a quantitative researcher and portfolio manager of statistical trading strategies at hedge funds and at Goldman Sachs. Prasad holds a PhD in Computer Science from CMU and BTech in Computer Science from IIT.