Machine Learning Pipelines - Joseph Bradley - Databricks

Digital Attribution Modeling Using Apache Spark-(Anny Chen and William Yan, Adobe)

Spark and Spark Streaming at Netfix-(Kedar Sedekar and Monal Daxini, Netflix)

Use of Spark MLib for Predicting the Offlining of Digital Media-(Christopher Burdorf, NBC Universal)

Advanced Data Science with Apache Spark-(Reza Zadeh, Stanford)

Building a Location Based Social Graph in Spark at InMobi-(Seinjuti Chatterjee and Ian Anderson, InMobi)

Visual Api Training

Real Time Fuzzy Matching with Spark and Elastic Search-(Sonal Goyal, Nube)

Advanced Data Science with Apache Spark-(Reza Zadeh, Stanford)

Dev Ops Training

Cassandra and Spark: Optimizing for Data Locality-(Russell Spitzer, DataStax)

Spark in the Wild: An In-Depth Analysis of 50+ Production Deployments-(Arsalan Tavakoli Shiraji, Databricks)

IndexedRDD: Efficeint Fine-Grained Updates for RDD's-(Ankur Dave, UC Berkeley)

A Data Frame Abstraction Layer for SparkR-(Chris Freeman, Alteryx)

How to Boost 100x Performance for Real World Application with Apache Spark-(Grace Huang and Jiangang Duan, Intel)

Accelerating Innovation with Spark-(Beth Smith, IBM)

Large-Scale Lasso and Elastic-Net Regularized Generalized Linear Models (DB Tsai and Steve Hillion, Netflix and Alpine Data Labs)

Spark at NASA/JPL-(Chris Mattmann, NASA/JPL)

Making Sense of Spark Performance-(Kay Ousterhout, UC Berkeley)

Spark and Hadoop at Production Scale-(Anil Gadre, MapR)