_productionizing-h2o-models-with-apache-spark-jakub-hava-h20-ai-and-michal-malohlava-h20-ai.jpg "Productionizing H2O Models with Apache Spark Jakub Hava (H20.ai) and Michal Malohlava (H20.ai) from h2o python Video Screenshot Preview")
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⏲ Duration: 18 min 85 sec
✓ Published: 11-Jun-2018
✓ Published: 11-Jun-2018
Description:
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.nnTherefore, we developed Sparkling Water that embeds H2O machine learning library of
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.nnTherefore, we developed Sparkling Water that embeds H2O machine learning library of
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