Integration

This article explains how to use Red Hat OpenShift AI in the Developer Sandbox for Red Hat OpenShift to create and deploy models.

Dive into the second edition of Camel integration quarterly digest, covering the

While the VMDK format is open source, they are traditionally used within the VMware family of solutions, such as VMware Workstation and VMware vSphere. In this post, we will describe how the previously created VMDK disk can be used as the basis for a virtual machine hosted in a VMware vSphere environment.

Image mode for Red Hat Enterprise Linux (RHEL) combines the benefits and methodologies of container technologies with the ability to operate within traditional virtual machine environments. By enabling a familiar container image assembly process that supports several popular output types, end users have the flexibility to choose where and how they want to operate.

One of the supported output formats is a Virtual Machine Disk (VMDK), which is commonly used within VMware’s supported virtualization platforms, like vSphere, but support is also available elsewhere, including VirtualBox. Bridging the gap between traditional operating environments and cloud-native technologies is one of the benefits that image mode provides. Over the course of this series, we will describe the process for creating image mode for RHEL-based bootc images for use within VMware environments, that, when included within reusable templates, will enable a consistent method for delivering virtual environments. Plus, several different methods will also be provided that illustrate how the generation of the various assets can be automated to create a repeatable factory for delivering bootc images to meet the demands of today and tomorrow.

This example demonstrates how to use the OpenShift Cluster Observability Operator, which provides a full monitoring stack you can configure to meet your needs.

Explore how to use OpenVINO Model Server (OVMS) built on Intel's OpenVINO toolkit to streamline the deployment and management of deep learning models.

This solution demonstrates an implementation to build a platform that synthesizes conversations across multiple different communication channels and services.

Learn how to use Satellite automation to schedule and perform repetitive tasks, such as querying Red Hat Insights APIs to generate and email CSV reports.

A lightweight, high-performance, robust, event streaming platform.

Learn how to set up Red Hat build of Keycloak as an Identity Broker on OpenShift using Active Directory Federation Services as a SAML 2.0 Identity Provider.

Get tips on managing and preserving Kafka Connect offsets smoothly starting from AMQ Streams 2.6 (Kafka 3.6).

Learn how to get started with Red Hat Connectivity Link, which provides application connectivity and API management in hybrid and multicloud environments.

Red Hat build of Apache Camel 4.4 is a versatile toolkit for enterprise

This guide will walk you through the process of setting up RStudio Server on Red Hat OpenShift AI and getting started with its extensive features.

The Edge to Core Pipeline Pattern automates a continuous cycle for releasing and deploying new AI/ML models using Red Hat build of Apache Camel and more.

This article guides you through migrating from Red Hat single sign-on to Red Hat build of Keycloak, a next-generation identity and access management solution.

Learn how you can achieve multicluster admission control and auditing using the Gatekeeper operator and Red Hat Advanced Cluster Management for Kubernetes.

Dive into the inaugural edition of Camel integration quarterly, covering the

Discover how to simplify your migration path to the Red Hat build of Apache Camel from Red Hat Fuse using a template-based solution pattern.

APIs evolve along with changing business needs and the technology landscape. This article provides strategies for navigating change with a practical example.

Product

Enables cryptographic signing, verification of software and provenance metadata

In the ever-evolving landscape of software development, the quest for

Learn how to create a Java application that uses AI and large-language models (LLMs) by integrating the LangChain4j library and Red Hat build of Quarkus.