Скачать или смотреть APAC Meetup: Enhancing Spark with H2O's AutoML using Sparkling Water

This presentation was recorded on October 15, 2020.

Slides from the presentation can be accessed here: https://bit.ly/3k1Q6f1

Learn more about how you can integrate large scale data preprocessing with Machine Learning using Sparkling Water. Sparkling Water enables training H2O-3 models leveraging Apache Spark clusters in a distributed manner. It also allows for using trained H2O-3 and Driverless AI models inside Apache Spark.

We will demonstrate model training together with hyper-parameter tuning (Cartesian and Random GridSearch with time constraint) of various algorithms, using AutoML – training meta-model combining different algorithms, hyper-parameter search and stacking (Ensemble method) all using Spark Pipeline API. We will also demonstrate how target encoding can be used with the Sparkling Water API.

What will users learn:
How to use H2O's GridSearch in Sparkling Water environment
How to use AutoML in Sparkling Water environment
How to put the trained models into production