Solutions Library

This post illustrates how with the help of Intel® artificial intelligence and machine learning, Ziva Dynamics is transforming how filmmakers create visual effects.

This post illustrates how the OpenVINO™ Model Server extends workloads across Intel® hardware (including accelerators) and maximizes performance across computer vision accelerators—CPUs, integrated GPUs, Intel Movidius VPUs, and Intel FPGAs

This document illustrates how, in addition to being a common platform for inference workloads, Intel Xeon Scalable processor is a competitive platform for both training and inference.

This document explores deep learning systems for image recognition in health and life sciences (HLS) and how Intel’s portfolio of products for artificial intelligence (AI) is helping make HLS AI solutions a reality.

This document describes the setup, installation and procedure to run distributed Deep Learning training and inference using TensorFlow with Uber Horovod library on Intel® Xeon® based infrastructure. The steps required to run the benchmark can vary depending on the user’s environment. In case of a large cluster with the order of hundreds or thousands of nodes, we provide sample scripts that use the SLURM scheduler. Alternatively, we also list out steps for smaller systems that may not have such a scheduler configured. Furthermore, we also provide scripts to build a singularity image for ease of deployment.

This White Paper illustrates how Julia Computing and Intel are powering the next big wave in Artificial Intelligence with high performance, advanced Machine Learning and Deep Learning based on Intel® architecture.

This solution brief highlights Ammune Defense Shield, an artificial intelligence (AI)-based self-learning cyberdefense system from L7 Defense* that reacts quickly to protect against extgeneration scripted or artificial intelligence (AI)-based cyberattacks. These attacks can evolve faster than humans can respond, but Ammune starts quickly and changes its defenses to match changes in the attack.

The purpose of this article is to illustrate how enterprises can start implementing AI projects right away in their existing data centers with the flexibility that Intel Xeon processors offer and with powerful tools Lenovo provides to realize the benefits of AI.

This paper introduces an image-based house recommendation system that was built between MLSListings* and Intel® using BigDL1 on Microsoft Azure*. Using Intel’s BigDL distributed deep learning framework, the recommendation system is designed to play a role in the home buying experience through efficient index and query operations among millions of house images.

This is an educational white paper on transfer learning, showcasing how existing deep learning models can be easily and flexibly customized to solve new problems. One of the biggest challenges with deep learning is the large number of labeled data points that are required to train the deep learning models to sufficient accuracy.