Video: Extreme-scale Data Science Using Spark

// 11.14.2016 // Data Science

At the Spark Summit in Brussels, MediaMath’s SVP of Data Science, Prasad Chalasani, gave an invited keynote talk, Extreme Scale Ad-Tech at MediaMath with Spark and Databricks.

MediaMath’s demand-side platform responds to over 200 billion ad-opportunities daily, and leverages massive amounts of data to power smarter digital marketing. We use Spark heavily both in production and R&D to develop innovative, proprietary, and scalable solutions to multiple large-scale data problems, such as:

  1. Training Machine-learning models for predicting conversion probability given an ad-impression
  2. Measuring causal effectiveness of advertising using randomized tests
  3. Estimating audience reach for specified targeting criteria.
  4. Finding deviceIDs belonging to the same user based on possibly noisy external deterministic information.
  5. Attributing clicks and conversions.
  6. Estimating win-rate as a function of bid-price and impression features.

A Picture of Travis Barnes

TRAVIS BARNES

Travis Barnes is the Technology Communications Manager at MediaMath, where he helps the engineering teams communicate the massive challenges that they tackle to the rest of the world. When not in the office, he is exploring Brooklyn’s music scene or digging through the crates of his local record store.
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