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Last updated : Apr 05, 2016
Not on the current edition
This blip is not on the current edition of the Radar. If it was on one of the last few editions it is likely that it is still relevant. If the blip is older it might no longer be relevant and our assessment might be different today. Unfortunately, we simply don't have the bandwidth to continuously review blips from previous editions of the Radar Understand more
Apr 2016
Trial ? Worth pursuing. It is important to understand how to build up this capability. Enterprises should try this technology on a project that can handle the risk.

Predictive analytics are used in more and more products, often directly in end user-facing functionality. H2O is an interesting open source package (with a startup behind it) that makes predictive analytics accessible to development teams, offering straightforward use of a wide variety of analytics, great performance and easy integration on JVM-based platforms. At the same time it integrates with the data scientists’ favorite tools, R and Python, as well as Hadoop and Spark.

Nov 2015
Trial ? Worth pursuing. It is important to understand how to build up this capability. Enterprises should try this technology on a project that can handle the risk.

Predictive analytics are used in more and more products, often directly in end user-facing functionality. H2O is an interesting open source package (with a startup behind it) that makes predictive analytics accessible to development teams, offering straightforward use of a wide variety of analytics, great performance and easy integration on JVM-based platforms. At the same time it integrates with the data scientists’ favorite tools, R and Python, as well as Hadoop and Spark.

May 2015
Assess ? Worth exploring with the goal of understanding how it will affect your enterprise.

Predictive analytics are used in more and more products, often directly in end-user facing functionality. H2O is an interesting new open source package (with a startup behind it) that makes predictive analytics accessible to project teams due to its easy-to-use user interface. At the same time it integrates with the data scientists’ favourite tools, R and Python, as well as Hadoop and Spark. It offers great performance and, in our experience, easy integration at runtime, especially on JVM-based platforms.

Published : May 05, 2015
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