Schedule: Media, Marketing, Advertising sessions

NMC (Nielsen Marketing Cloud) provides customers (both marketers and publishers) with real-time analytics tools to profile their target audiences. To achieve that, the company needs to ingest billions of events per day into its big data stores in a scalable, cost-efficient way. Itai Yaffe explains how NMC continuously transforms its data infrastructure to support these goals. Read more.

Alexander Thomas and Alexis Yelton demonstrate how to use Spark NLP and Apache Spark to standardize semistructured text, illustrated by Indeed's standardization process for résumé content. Read more.

Spotify's mission is "to match fans and artists in a personal and relevant way." Mounia Lalmas shares some of the (research) work the company is doing to achieve this, from using machine learning to metric validation, illustrated through examples within the context of home and search. Read more.

Maryam Jahanshahi explores exponential family embeddings: methods that extend the idea behind word embeddings to other data types. You'll learn how TapRecruit used dynamic embeddings to understand how data science skill sets have transformed over the last three years, using its large corpus of job descriptions, and more generally, how these models can enrich analysis of specialized datasets. Read more.

Simona Meriam explains how Nielsen Marketing Cloud (NMC) used to manage its Kafka consumer offsets against Spark-Kafka 0.8 consumer and why the company decided to upgrade from Spark-Kafka 0.8 to 0.10 consumer. Simona reviews the problems encountered during the upgrade and details the process that led to the solution. Read more.

The advent of "fake news" has led us to doubt the truth of online media, and advances in machine learning give us an even greater reason to question what we are seeing. Despite the many beneficial applications of this technology, it's also potentially very dangerous. Alex Adam explains how synthetic videos are created and how they can be detected. Read more.

Identifying relevant documents quickly and efficiently enhances both user experience and business revenue every day. Sophie Watson demonstrates how to implement learning-to-rank algorithms and provides you with the information you need to implement your own successful ranking system. Read more.

The success of deep learning has reached the realm of structured data in the past few years, where neural networks have been shown to improve the effectiveness and predictability of recommendation engines. Oliver Gindele offers a brief overview of such deep recommender systems and explains how they can be implemented in TensorFlow. Read more.