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.

The Dell Reference Architecture environment is a full-stack solution that includes Dedicated Dell PowerSwitch High-Speed Networking, Dell PowerEdge Accelerator Optimized Compute nodes (XE9680 and R760xa servers), and Dell PowerScale Storage (F600 Array) as the hardware components and includes multiple MLOps and Kubernetes Platform solutions.

NVIDIA DGX H100 integration and validation environment for enterprise AI workflows.

The NVIDIA Blueprints for generative virtual screening demonstrate how generative AI and accelerated NVIDIA NIM microservices can be leveraged to design optimized small molecules more intelligently and efficiently.

The AI Proving Ground GPU-as-a-Service environment is a fully automated solution that enables WWT engineers to build physical server environments with different server, CPU, GPU, and Operating System options.

In this self-paced lab you will be introduced to the Unisphere UI for Unity XT. Topics covered include Settings, Dashboard and System categories.

Welcome to WWT's introductory lab on the Dell PowerStore storage platform. This foundational lab is designed to provide systems engineers with a comprehensive understanding of PowerStore's capabilities. You will learn how to monitor and manage the PowerStore storage array, provision both block and file volumes, and configure replication processes. This hands-on lab can be completed in under an hour, offering a quick yet thorough exploration of the PowerStore platform's features and functionalities. Welcome to WWT's introduction to the Dell PowerStore storage platform. This foundational lab is designed for systems engineers to gain hands-on experience with PowerStore. The lab covers essential tasks such the Initial Configuration, Dashboard Navigation, Storage Management, Host Management, Protection, Internal and External Migrations, Hardware Details, and Settings. Expect to complete this lab in under an hour, providing you with a comprehensive understanding of PowerStore's capabilities.

Discover the Dell Reference Architecture, a full-stack solution optimized for generative AI with cutting-edge Dell and AMD technologies. Experience hands-on configuration, MLOps, and Kubernetes platforms, while validating performance and efficiency. Accelerate your AI deployment and optimize data center operations with this comprehensive, high-performance environment.

Welcome to WWT's introduction to PowerFlex Operations Foundations Lab. This lab is designed to provide an introduction to the PowerFlex software-defined storage platform with a brief history, review of the components of PowerFlex, overview of the dashboard interface, provisioning, and local data protection.

Welcome to WWT's introduction to Ansible automation for the Dell PowerStore storage platform. In this lab, you will learn the basics of how to automate fundamental Dell EMC PowerStore storage array administrative tasks utilizing Ansible. This lab is estimated to take under 1 hour to complete. The intended lab audience is a systems engineer with a basic understanding of PowerStore and Ansible components.

In this self-paced lab you will be introduced to the Unisphere UI for PowerMax. Topics covered include system settings, home page, performance reporting and provisioning.

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.