Red Hat OpenShift AI

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.

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.

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.

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

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

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

Walk through the basics of fine-tuning a large language model using Red Hat OpenShift Data Science and HuggingFace Transformers.

Learn how to use the Red Hat OpenShift Data Science platform and Starburst to develop a fraud detection workflow with an AI/ML use case.

Learn why graphics processing units (GPUs) have become the foundation of artificial intelligence and how they are being used.

Intel AI tools save cloud costs, date scientists' time, and time spent developing models. Learn how the AI Kit can help you.

OpenVINO helps you tackle speech-to-text conversion, a common AI use case. Learn more.

Once you have data, how do you start building a PyTorch model? This learning path shows you how to create a PyTorch model with OpenShift Data Science.

OpenShift AI gives data scientists and developers a powerful AI/ML platform for building AI-enabled applications. Data scientists and developers can collaborate to move from experiment to production in a consistent environment quickly.

Collecting and understanding the data gathered through AI/ML workloads.

Managed cloud services provide stable training and workload platforms for

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

Big data analysis platforms are rapidly becoming a core tool in mainstream applications development. However, while the power of these technologies is undeniable, the promise of elasticity through wide cluster scaling is often elusive. This talk will demonstrate the use of Openstack Sahara's cluster provisioning and elastic data processing features to create development, research, test, and production clusters and integrate them into a larger application.