Organizations have made large investments in modernizing their data infrastructure and operations, but most still struggle to drive maximum value from their data.  Many companies experimented with building large teams of expert data scientists, and while this approach did produce some valuable models, the cost was high and the timeframes long.  In addition, many of […]

Deploying real-world applications using bare YAML files to Kubernetes is a rather complex task, and H2O is no exception. As demonstrated in one of the previous blog posts. Greatly simplified, a cluster of H2O open source machine learning nodes is brought up in the following manner: A headless service to make initial node discovery and clustering […]

Sparkling Water can now be executed inside the Kubernetes cluster. Sparkling Water provides a Beta version of Kubernetes support in a form of nightlies. Both Kubernetes deployment modes, cluster and client, are supported. Also, both Sparkling Water backends and all clients are also ready to be tested. Sparkling Water in Kubernetes is currently in open […]

H2O is an open-source, in-memory platform for distributed, scalable machine learning. A perfect match for deployment on a Kubernetes cluster, the very modern way of deploying, serving & scaling applications. With the major release 3.30.0.1, released in Q1 2020, H2O obtained first class Kubernetes support. This article explains how to create H2O deployment on Kubernetes. It also covers the […]

Organizations globally are operationalizing containers and Kubernetes to accelerate Machine Learning lifecycles as these technologies provide data scientists and software developers with much needed agility, flexibility, portability, and scalability to train, test, and deploy ML models in production. Red Hat OpenShift is the industry’s most comprehensive Kubernetes hybrid cloud platform. It provides the above benefits […]