Some applications are hard to manage in Kubernetes, requiring lots of manual labor and time invested to operate them. Some applications might be hard to set up, some need special care for restarts,...

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 clu...

The H2O Open Source is an in-memory platform for distributed, scalable machine learning. A perfect match for deployment on a Kubernetes cluster, the very modern way of deploying, serving & scal...

H2O is in it’s core a platform for distributed, in-memory computing. On top of the distributed computation platform, the Machine learning algorithms are implemented. At H2O, we design every operati...

Machine-learning models created with H2O may be exported in two basic ways: Binary format, Model Object, Optimized (MOJO). An H2O model can be saved in a binary format, which is tied to the...

A common use-case when working with H2O open-source machine learning platform is to load data from Amazon S3 buckets. However, not all buckets are publicly accessible. In the case of H2O users load...

There were two releases shortly after each other. First, on December 21st, there was a minor (fix) release 3.22.0.3. Immediately followed by a more major release (but still on 3.22 branch) codename...

At H2OWorld, the greatest minds in AI, data science and mathematics gather during one huge machine-learning oriented community event. There are series of trainings and presentations focused not onl...

H2O-3, the open source machine learning platform offers several algorithms based on decision trees. In latest stable release, we’ve made it possible for data scientists and developers to inspect th...

H2O is the world’s number one machine learning platform. It is an open-source software, the H2O-3 GitHub repository is available for anyone to start hacking. This hands-on guide aims to explain the...