Name: Fresh Season - Fresh Water: Productionizing H2O Models Using Apache Spark
Start: 2019-09-26T18:00:00+02:00
End: 2019-09-26T20:00:00+02:00
Location: Ataccama office

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, H2O.ai, open source machine learning and artificial intelligence platform, developed Sparkling Water that embeds H2O machine learning library of advanced algorithms into the Spark ecosystem and exposes them via pipeline API. Furthermore, the algorithms benefit from H2O MOJOs – Model Object Optimized – a powerful concept shared across entire H2O platform to store and exchange models. The MOJOs are designed for effective model deployment with focus on scoring speed, traceability, exchangeability, and backward compatibility. This talk will explain the architecture of Sparkling Water with focus on integration into the Spark pipelines and MOJOs.

Jakub Háva will demonstrate creation of pipelines integrating H2O machine learning models and their deployments using Scala or Python. Furthermore, Jakub will show how to utilize pre-trained model MOJOs with Spark pipelines.

Katerina Lesch

HUG: Big Data Meetup Prague

Technology

Big Data

Artificial Intelligence

Machine Learning

Cloud Computing

Data Analytics

Hadoop

Apache Spark

Data Mining

NoSQL

Business Analytics

Big Data for Social Media

Software Architecture

Scalability

Fresh Season - Fresh Water: Productionizing H2O Models Using Apache Spark

Ataccama office

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HUG: Big Data Meetup Prague

Fresh Season - Fresh Water: Productionizing H2O Models Using Apache Spark

HUG: Big Data Meetup Prague

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