AI/ML

Accurately labeled data is crucial for training AI models. Learn how to prepare and label a custom dataset using Label Studio in this tutorial.

Learn how to configure Red Hat OpenShift AI to train a YOLO model using an already provided animal dataset.

A common platform for machine learning and app development on the hybrid cloud.

Red Hat Enterprise Linux (RHEL) 9.4 is now generally available (GA). Learn about the latest enhancements that improve the developer experience.

Applications based on machine learning and deep learning, using structured and unstructured data as the fuel to drive these applications.

Learn how to install the Red Hat OpenShift AI operator and its components in this tutorial, then configure the storage setup and GPU enablement.

Red Hat provides AI/ML across its products and platforms, giving developers a portfolio of enterprise-class AI/ML solutions to deploy AI-enabled applications in any environment, increase efficiency, and accelerate time-to-value.

Learn how to deploy single node OpenShift on a physical bare metal node using the OpenShift Assisted Installer to simpify the OpenShift cluster setup process.

Learn how to create a Red Hat OpenShift AI environment, then walk through data labeling and information extraction using the Snorkel open source Python library.

Enterprise-grade artificial intelligence and machine learning (AI/ML) for developers, data engineers, data scientists, and operations.

Integrate generative AI in your applications with Podman AI Lab, an open source extension for working with large language models in a local environment.

Learn how to access a large language model using Node.js and LangChain.js. You

Discover the benefits of KServe, a highly scalable machine learning deployment tool for Kubernetes.

This blog summarises key changes for Red Hat Ansible Lightspeed with IBM watsonx

VMware Cloud Foundation 5.1 now supports Red Hat OpenShift Container Platform 4.13 and NVIDIA AI Enterprise, offering automated, consistent infrastructure and more.

Learn how Intel Graphics Processing Units (GPUs) can enhance the performance of machine learning tasks and pave the way for efficient model serving.

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.

Discover how to integrate cutting-edge OpenShift AI capabilities into your Java applications using the OpenShift AI integration with Quarkus.

Join Red Hat Developer for the software and tutorials to develop cloud applications using Kubernetes, microservices, serverless and Linux.

MLOps with Kubeflow Pipelines can improve collaboration between data scientists and machine learning engineers, ensuring consistency and reliability at every stage of the development workflow.

Discover how to use machine learning techniques to analyze context, semantics, and relationships between words and phrases indexed in Elasticsearch.

Explore features that enhance automation productivity for developers in Ansible Lightspeed with IBM watsonx Code Assistant, now generally available.

Learn how to communicate with OpenAI ChatGPT from a Quarkus application using the ChatGPT API in this demo.

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