25 results found

Navigating the Waters of GenAI with WWT & AWS

As an experienced guide, WWT can help enterprise organizations at every level of GenAI aptitude. Read on for use cases from WWT and AWS that showcase how to ride the wave of AI innovation toward rapid ROI and scalability.
Article
•Nov 4, 2024

How state and local governments are using generative AI to serve citizens better

This first part of the e-book explores four concrete ways WWT has helped agencies deploy generative AI solutions that streamline operations and free staff for higher-value work. The second part offers tips and guidance for how to get started with AI adoption at your agency.
eBook
•Feb 23, 2026

Unveiling the True Potential of Cloud AI With AWS

This article explores the advantages of partnering with WWT to utilize AWS AI services. It highlights key factors—from significant efficiencies to important safeguards, and beyond‒to guide you on a rapid, cost-effective, and efficient path to a successful AI implementation.
Article
•Aug 16, 2024

Partner POV | The Hidden Price Tag: Uncovering Hidden Costs in Cloud Architects with the AWS Well-Architected Framework

AWS and cloud computing changed how businesses operate. Organizations now store, process, and manage data in the cloud at scale while treating compute resources as a utility.
Partner Contribution
•Apr 15, 2026

Enterprise Cloud Transformation: Practical Approaches to Migration and Modernization with WWT & AWS

Success requires more than cloud services—it demands risk mitigation, coordination, and proven methodologies. WWT, an AWS Premier Tier partner, addresses these challenges with its Advanced Technology Center (ATC) for risk-free testing and validation.
Article
•Aug 4, 2025

Partner POV | The generative AI advantage

A leader's guide to using your data as a differentiator
eBook
•Dec 5, 2024

Partner POV | Your AI is Only as Good as Your Data

Generative AI is undoubtedly one of the most transformative and disruptive technologies of our time. These powerful models can generate human-like text, images, code, and more in ways that seem almost miraculous. However, behind the awe-inspiring outputs lies an even more incredible foundation – the massive datasets and robust data operations required to make generative AI possible.
Partner Contribution
•Oct 31, 2024

Partner POV | Migrate VMware virtual machines (VMs) to Amazon EC2 with the AWS Application Migration Service Replication Agent

In this blog post, we will walk you through the step-by-step process of completing VMware virtual machine (VM) migrations to Amazon Elastic Compute Cloud (Amazon EC2) using the (Application Migration Service). Moreover, we will show how to apply a custom post-launch action script to remove proprietary VMware tools from the migrated VMs.
Partner Contribution
•Oct 29, 2024

Partner POV | Accelerate software development and leverage your business data with generative AI assistance from Amazon Q

We believe generative artificial intelligence (AI) has the potential to transform virtually every customer experience. To make this possible, we're rapidly innovating to provide the most comprehensive set of capabilities across the three layers of the generative AI stack.
Partner Contribution
•Dec 4, 2024

Deploying generative AI applications with NVIDIA NIMs on Amazon EKS

Amazon EKS is a managed service for running Kubernetes workloads on AWS. We can use EKS to orchestrate NVIDIA NIM (plural: NIMs) pods across multiple nodes, because it automatically manages the availability and scalability of the Kubernetes control plane nodes responsible for scheduling containers, managing application availability, storing cluster data, and other key tasks.
Partner Contribution
•Dec 3, 2024

Partner POV | Create a generative AI-based application builder assistant using Amazon Bedrock Agents

In this post, we set up an agent using Amazon Bedrock Agents to act as a software application builder assistant.
Partner Contribution
•Nov 21, 2024

Partner POV | Gain operational insights for NVIDIA GPU workloads using Amazon CloudWatch Container Insights

As machine learning models grow more advanced, they require extensive computing power to train efficiently. Many organizations are turning to GPU-accelerated Kubernetes clusters for both model training and online inference. However, properly monitoring GPU usage is critical for machine learning engineers and cluster administrators to understand model performance and to optimize infrastructure utilization.
Partner Contribution
•Dec 4, 2024