AI/ML: Persistent workspaces for multiple users
The first challenge for an AI/ML practitioner is gathering the necessary data to feed the process. The solution? Advanced planning algorithms that organize data better than humans in far less time.
AI/ML resources
Create a Red Hat OpenShift AI environment
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.
Empower conversational AI at scale with KServe
Discover the benefits of KServe, a highly scalable machine learning deployment tool for Kubernetes.
Run Red Hat OpenShift Container Platform 4.13 on VMware Cloud Foundation 5.1 with NVIDIA AI Enterprise
VMware Cloud Foundation 5.1 now supports Red Hat OpenShift Container Platform 4.13 and NVIDIA AI Enterprise, offering automated, consistent infrastructure and more.
Intel GPUs and OVMS: A winning combination for deep learning efficiency
Learn how Intel Graphics Processing Units (GPUs) can enhance the performance of machine learning tasks and pave the way for efficient model serving.
How to use LLMs in Java with LangChain4j and Quarkus
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.
How to integrate Quarkus applications with OpenShift AI
Discover how to integrate cutting-edge OpenShift AI capabilities into your Java applications using the OpenShift AI integration with Quarkus.
