Artificial intelligence

Learn how Thoth gathers and analyzes data to create advice through a case study about a recent runtime issue inspection when importing TensorFlow 2.1.0.

Explore Open Data Hub 0.8's improved support for mixing ODH and Kubeflow components, CI/CD, Kubeflow monitoring, distributed machine learning, and more.

Discover the updates in Open Data Hub 0.7, including support for Kubeflow 1.0 and increased component testing for OpenShift continuous integration.

Learn how to experiment with your data models with Kale (a Kubeflow extension using JupyterLab's UI) to convert your notebooks to Kubeflow pipelines.

Explore three options for customizing Open Data Hub or Kubeflow deployments: Edit manifests in a fork, create repositories with overrides, and add overlays.

As edge computing's importance increases, let's look at the best practices application developers should consider when developing for the edge.

Check out the Open Data Hub team's plans for upcoming releases: making Kubeflow 1.0 available on Red Hat OpenShift, improving Kubeflow CI, and more.

Learn how to set up a local environment to develop and test the Quarkus Infinispan client with Red Hat Data Grid 8.0 on CodeReady Containers.

Explore the bug fixes provided in Open Data Hub 0.6.1's Kubeflow Operator, manifests, testing, and continuous integration.

Explore the changes in Open Data Hub version 0.6, including significant changes to the overall architecture as well as component updates and additions.

Learn how important spatial data is, and tour the tools you might need to use this massive amount of data well and with as little manual work as possible.

Discover the new and improved features in Red Hat Data Grid 8.0, including a new server architecture, an improved REST API, and enhanced observability.

We describe how to use Open Data Hub and Kubeflow pipelines, both of which use Argo as the AI/ML pipeline tool.

We show the potential of project Thoth's infrastructure running in Red Hat Openshift and how it can collect performance observations.

Guidance for creating ABI compatible Python wheels for RHEL and the new manylinux2014 standard

We explain how to include large data files into the body of an executable program so that it's there when the program runs.

Check out the instructor-led labs in the Emerging Technologies track of Red Hat Summit 2019, coming up May 7-9 in Boston.

This presentation will cover two projects from sig-big-data: Apache Spark on Kubernetes and Apache Airflow on Kubernetes. We will give an overview of the current state and present the roadmap of both projects, and give attendees opportunities to ask questions and provide feedback on roadmaps.

Video of Kubecon 2018, explore the various integrations that have enabled Kubeflow to quickly emerge as the de-facto machine learning toolkit for Kubernetes.

Integrate Cloudera's Apache Impala implementation as a Data Source in Red Hat's JBoss Data Virtualization. The goal of this post is to import data from a Cloudera Impala instance, manipulate it and expose that data as a data service

With the help of Infinispan, you can take advantage of state of the art distributed data processing capabilities.