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Productionizing H2O Models with Apache Spark (Kuba)

451 views · Published 20 September 2018 · 31:25 · Indexed 1 October 2026

Channel: Databricks · 2018 · Science & Technology

Jakub Hava (or "Kuba") is a cluster monitoring expert at H2O. Kuba shares about H2O's current Sparkling Water Project.

Spark pipelines represent a powerful concept to support productionizing machine learning workflows. Their API allows to combine data processing with machine learning algorithms and opens opportunities for integration with various machine learning libraries. However, to benefit from the power of pipelines, their users need to have a freedom to choose and experiment with any machine learning algorithm or library.

Therefore, we developed Sparkling Water that embeds H2O machine learning library of advanced algorithms into the Spark ecosystem and exposes them via pipeline API. We’ll demonstrate creation of pipelines integrating H2O machine learning models and their deployments using Scala or Python.

To learn more: https://databricks.com/blog/2015/12/02/databricks-and-h2o-make-it-rain-with-sparkling-water.html
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Productionizing H2O Models with Apache Spark (Kuba)

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