Widely available GPU-accelerated servers, combined with better hardware and popular programming languages like Python and C/C++, along with frameworks such as PyTorch, TensorFlow and JAX, simplify the development of GPU-accelerated ML applications. These applications serve diverse purposes, from medical research to self-driving vehicles, relying on large datasets and GPU clusters for training deep neural networks. Inference frameworks apply knowledge from trained models to new data with optimized clusters for performance. The learning cycles involved in AI workloads can take days or weeks, and high-latency communication between server clusters can significantly impact completion times or result in failure. AI workloads demand low-latency, lossless networks, requiring appropriate hardware, software features, and configurations. This article will explain advanced queueing solutions used by all the major OEMs in the Network Operating Systems (NOS) that support ECN and PFC.

WWT's approach to full-stack excellence provides CX leaders with a framework for unlocking efficiency, growth and fresh digital experiences for their customers.

This article discusses the importance of deploying AI applications and training models using distributed computing and the need for significant computational resources. It highlights the role of network efficiency and scalability in large-scale AI deployments.

A brief history of digital twins and their role in training, simulating, planning, researching and testing complex AI/ML models at massive scale.

Looking for a flexible, cloud-based artificial intelligence solution to complement your Contact Center environment? Google CCAI might be for you.

AI is revolutionizing the way businesses interact with their customers. Here are the top ways that contact center leaders should leverage AI to yield the biggest ROI.

World Wide Technology, Intel, N50, and Hope Ignites, formerly Boys Hope Girls Hope, collaborate to bridge the digital divide in education. Utilizing AI and open-source LLMs on Intel servers, they collaborated to create an offline, multilingual educational chatbot for underserved regions. This scalable solution transforms learning experiences, ensuring accessibility and engagement for students globally.

Microsoft 365 Copilot Wave 2 marks a major leap in AI-powered productivity, introducing enhanced reasoning, cross-app intelligence and new AI agents. Explore key updates across Word, Excel, Outlook, Teams and PowerPoint, and understand how your organization can prepare to leverage these new capabilities.

The AI Proving Ground enables organizations to quickly, confidently and safely develop transformational AI solutions that deliver real business results in a fraction of the time.

Discover how leveraging the cloud can help organizations accelerate AI adoption.

Holistic automation and AI capabilities are possible when organizations mature their data practices.

Protecting AI models and the development environment is a major challenge given the complexity of underlying big data platforms and the mathematics required to understand attacks against modern AI algorithms.