Tuning and Monitoring Deep Learning on Apache Spark: Spark Summit East talk by Tim Hunter

Tuning and Monitoring Deep Learning on Apache Spark: Spark Summit East talk by Tim Hunter

Geospatial Analytics at Scale with Deep Learning and Apache Spark-Tim Hunter & Raela Wang-DatabricksПодробнее

Geospatial Analytics at Scale with Deep Learning and Apache Spark-Tim Hunter & Raela Wang-Databricks

Building Deep Learning Powered Big Data: Spark Summit East talk by Jiao Wang and Yiheng WangПодробнее

Building Deep Learning Powered Big Data: Spark Summit East talk by Jiao Wang and Yiheng Wang

Geospatial Analytics at Scale with Deep Learning and Apache SparkRaela Wang Databricks,Tim Hunter DaПодробнее

Geospatial Analytics at Scale with Deep Learning and Apache SparkRaela Wang Databricks,Tim Hunter Da

TensorFrames: Deep Learning with TensorFlow on Apache Spark (Tim Hunter)Подробнее

TensorFrames: Deep Learning with TensorFlow on Apache Spark (Tim Hunter)

Spark Autotuning: Spark Summit East talk by: Lawrence SpracklenПодробнее

Spark Autotuning: Spark Summit East talk by: Lawrence Spracklen

Scaling Genetic Data Analysis with Apache Spark: Spark Summit East talk by Cotton SeedПодробнее

Scaling Genetic Data Analysis with Apache Spark: Spark Summit East talk by Cotton Seed

Expanding Apache Spark Use Cases in 2.2 and Beyond - Matei Zaharia, Tim Hunter & Michael ArmbrustПодробнее

Expanding Apache Spark Use Cases in 2.2 and Beyond - Matei Zaharia, Tim Hunter & Michael Armbrust

From Pipelines to Refineries: Building Complex Data Applications with Apache Spark - Tim HunterПодробнее

From Pipelines to Refineries: Building Complex Data Applications with Apache Spark - Tim Hunter

Scaling Apache Spark MLlib to Billions of Parameters: Spark Summit East talk by Yanbo LiangПодробнее

Scaling Apache Spark MLlib to Billions of Parameters: Spark Summit East talk by Yanbo Liang

SparkLint: a Tool for Monitoring, Identifying and Tuning Inefficient Spark Jobs (Simon Whitear)Подробнее

SparkLint: a Tool for Monitoring, Identifying and Tuning Inefficient Spark Jobs (Simon Whitear)