filmov
tv
Extending Spark Machine Learning: Adding Your Own Algorithms and Tools
Показать описание
Apache Spark's machine learning (ML) pipelines provide a lot of power, but sometimes the tools you need for your specific problem aren't available yet. This talk introduces Spark's ML pipelines, and then looks at how to extend them with your own custom algorithms. By integrating your own data preparation and machine learning tools into Spark's ML pipelines, you will be able to take advantage of useful meta-algorithms, like parameter searching and pipeline persistence (with a bit more work, of course). With Holden Karau and Seth Hendrickson
About: Databricks provides a unified data analytics platform, powered by Apache Spark™, that accelerates innovation by unifying data science, engineering and business.
Connect with us:
About: Databricks provides a unified data analytics platform, powered by Apache Spark™, that accelerates innovation by unifying data science, engineering and business.
Connect with us:
0:31:03
Extending Spark Machine Learning: Adding Your Own Algorithms and Tools
0:32:34
Extending Apache Spark ML: Adding Your Own Algorithms and Tools - Holden Karau and Nick Pentreath
0:32:04
Extending Spark Machine Learning Beyond Linear Regression by Holden Karau
0:11:15
Extending Spark Machine Learning Pipelines Going beyond wordcount with Spark ML
0:22:51
Extending Spark ML for your custom algorithms - Holden Karau
0:32:01
Extending Spark ML for Custom Models - Holden Karau
0:29:54
Extending Spark APIs Without Going Near Spark Source (Anna Holschuh)
0:30:15
Extending Spark ML to Deliver Fast, Scalable & Unified NLP
0:33:48
Extending Spark ML for Custom Models - Holden Karau (15-11-2017)
0:23:45
Extending Apache Spark – Beyond Spark Session Extensions
0:46:05
Advanced Spark Features - Matei Zaharia
0:33:20
How to Extend Apache Spark with Customized OptimizationsSunitha Kambhampati IBM
0:20:32
Scaling Machine Learning Feature Engineering in Apache Spark at Facebook
0:30:34
Extending Spark SQL API with Easier to Use Array Types Operations - Marek Novotny and Alex Vayda
0:04:32
Add Spark dependencies to the application
0:26:05
Get started building custom ML transformers and estimators with PySpark
0:30:42
Extending Spark with Java Agents (Jaroslav Bachorik)
0:50:56
Extending Spark Using Sparklyr | RStudio Webinar - 2017
0:46:01
Adding Rules to Apache Spark Catalyst
0:28:36
Building, Debugging, and Tuning Spark Machine Learning Pipelines - Joseph Bradley (Databricks)
0:40:32
Stranger Triumphs: Automating Spark Upgrades & Migrations at Netflix
0:24:30
The Killer Feature Store: Orchestrating Spark ML Pipelines and MLflow for Production
0:28:26
How to Productionize Your Machine Learning Models Using Apache Spark MLlib 2 x-Richard Garris
0:27:58
Building Machine Learning Algorithms on Apache Spark - William Benton
Комментарии