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 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.

The Intel Gaudi cluster supports a single Gaudi-1 Appliance with eight first-generation deep learning processors and a single Gaudi-2 Appliance with eight second-generation deep learning processors. Each HPC Appliance can leverage both local NVMe storage or High-Speed Storage systems via a dedicated 100GbE network fabric. Customers can leverage the environment to validate different AI Training or Inferencing solutions along with recording both performance and power metrics while they are performing their tests.

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.