Large-scale Deployments in the Enterprise

The combination of BlueData with represents a game-changing solution for large-scale AI / ML deployments in the enterprise. Organizations can get up and running quickly with distributed H2O machine learning environments at scale, in a multi-tenant architecture with a shared pool of resources for improved efficiency and reduced costs. They can operationalize machine learning pipelines and deliver faster time-to-value for AI initiatives — while ensuring security and high performance.

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Provisioning H2O with BlueData EPIC

With the BlueData EPIC software platform, data science teams can instantly spin up multi-node clusters for H2O Flow, Sparkling Water, and Driverless AI running on containers – whether on-premises, in the public cloud, or in a hybrid model. Through its App Store, BlueData EPIC provides pre-tested and pre-integrated Docker container images for distributed H2O environments. In just a few mouse clicks, users can have on-demand access to H2O’s best-in-class machine learning capabilities – using GPUs and/or CPUs for their containerized compute clusters.

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Optimize Compute and Storage Resources

BlueData offers a data connector to enable compute / storage separation for H2O. With BlueData, different containerized compute clusters for different workloads (including Spark, Kafka, Tensorflow, and/or H2O) can share access to a common data lake. This separation enables H2O users to easily connect their compute cluster to the data they need for large-scale distributed machine learning, while providing the ability to scale and optimize compute resources independent from data storage. The result is improved flexibility and resource utilization, along with reduced costs by eliminating data duplication and reusing existing storage investments.