NVIDIA Blueprints: PDF Ingestion, also known as NVIDIA-Ingest, or NV-Ingest, this blueprint is a scalable, performance-oriented document content and metadata extraction microservice. Including support for parsing PDFs, Word and PowerPoint documents, it uses specialized NVIDIA NIM microservices to find, contextualize, and extract text, tables, charts and images for use in downstream generative applications.

Red Hat OpenShift AI, formerly known as Red Hat OpenShift Data Science, is a platform designed to streamline the process of building and deploying machine learning (ML) models. It caters to both data scientists and developers by providing a collaborative environment for the entire lifecycle of AI/ML projects, from experimentation to production. In this lab, you will explore the features of OpenShift AI by building and deploying a fraud detection model. This environment is built ontop of Dell R660's and Intel Xeon's 5th generation processors.

This lab will provide access to an Openshift cluster running the F5 AI Gateway solution. We will walk through how the F5 AI Gateway routes requests to different models by either allowing them to pass through or, more importantly, securing them via prompt injection checking. We have also added a couple of other tests that will allow for language detection of that input that the F5 AI Gateway can also detect.

In this hands-on lab, you'll learn how to set up a disconnected installation of OpenShift, allowing you to deploy and manage OpenShift clusters in environments without direct internet access. You'll begin by downloading the necessary OpenShift components on a connected host, transferring them to a disconnected host, and proceeding with installing OpenShift on bare metal servers using the agent-based installer. By the end of this lab, you'll have a fully functional, disconnected OpenShift cluster. This lab is ideal for users working in secure or isolated environments where external connectivity is restricted or non-existent.

In this lab environment, you will walk through deploying your own OpenShift 4.11 cluster on vSphere. The deployment method walked through uses the 'Installer-Provisioned Infrastructure' method. Once deployed, use some of our linked use cases or carve your path to experience what OpenShift offers.

This lab will provide access to an Openshift cluster running the F5 AI Gateway solution. We will walk through how the F5 AI Gateway routes requests to different models by either allowing them to pass through or, more importantly, securing them via prompt injection checking. We have also added a couple of other tests that will allow for language detection of that input that the F5 AI Gateway can also detect.

This lab is an experience of an organization who aspires for digital transformation but fails to realize the technical quality and performance required to fulfill an innovative business objective. We'll walk through a scenario of a time-sensitive business strategy, common issues encountered during development and operations of a custom technology platform, then apply procedural adaptations which increase efficiency, security, and reliability for the technical foundation of the business solution.

Virtual CMTS (vCMTS) revolutionizes bandwidth management by virtualizing DOCSIS processing on x86 servers, paving the way for DOCSIS 4.0. Intel's Xeon 6 processors enhance encryption efficiency, while Red Hat's OpenShift Cloud Platform unifies workload management. This lab explores a deployment of vCMTS on OpenShift, showcasing performance insights via Grafana.

This lab focuses on implementing live person tracking using Intel's OpenVINO™, a toolkit for high-performance deep learning inference. The objective is to read frames from a video sequence, detect people within the frames, assign unique identifiers to each person, and track them as they move across frames. The tracking algorithm utilized here is Deep SORT (Simple Online and Realtime Tracking), an extension of SORT that incorporates appearance information along with motion for improved tracking accuracy.

This lab will walk you through one of Intel's AI Reference Kits to develop an optimized semantic segmentation solution based on the Visual Geometry Group (VGG)-UNET architecture, aimed at assisting drones in safely landing by identifying and segmenting paved areas. The proposed system utilizes Intel® oneDNN optimized TensorFlow to accelerate the training and inference performance of drones equipped with Intel hardware. Additionally, Intel® Neural Compressor is applied to compress the trained segmentation model to further enhance inference speed. Explore the Developer Catalog for information on various use cases.