Sign in to unlock valuable content and features from our AI-driven platform. Receive timely technology updates and the latest information from the solution providers who can help you realize your goals.
Start your journey by entering your name and email address below:
Please confirm your email address!
We are going to send a confirmation email to your email address to let you receive timely technology updates and the latest information from the solution providers who can help you realize your goals.
Is this you? Please confirm your name and email address below to receive the requested information.
Please check this box to confirm that you are opting-in to receive communications from HPEE and the data sharing outlined in our privacy policy.
Initializing
Loading
Getting Started with Generative AI on AWS
Organizations exploring generative AI often need clarity on responsible AI risks, hallucinations, privacy concerns, and cost challenges. The eBook, "Getting Started with Generative AI on AWS," outlines the issues agencies and institutions must consider with foundation models. Download the eBook to evaluate these considerations and contact HPE to discuss GenAI strategies tailored to your mission.
Please enter your information below to view this content:
Foundational models (FMs) are large-language models (LLMs) and multi-modal models that enable various capabilities, such as code generation and image creation from natural language descriptions. They can be easily customized for specific use cases like summarization and translation without needing to build a new machine learning model from scratch. FMs are pre-trained using vast amounts of unlabeled data, allowing them to deliver strong performance across multiple tasks.
How can organizations customize foundational models?
Organizations can customize foundational models through several methods: zero-shot learning, where users provide prompts to interact with the model; in-context learning, where examples are included in prompts to enhance output relevance; and fine-tuning, which involves training the model with a small number of labeled examples to tailor it for specific tasks. This approach is efficient and requires significantly less labeled data than developing a model from scratch.
What are the benefits of using AWS for generative AI?
AWS provides several benefits for building generative AI applications, including enterprise-grade security and privacy, access to leading foundational models, and a data-first approach. Additionally, AWS offers a highly performant and cost-effective infrastructure, enabling organizations to train their models and run inferences at scale. This allows businesses to leverage their data as a strategic asset to create customized and differentiated generative AI solutions.
Getting Started with Generative AI on AWS
published by HPE
Aruba, a Hewlett Packard Enterprise company, is a leading provider of next-generation network access solutions for the mobile enterprise. The company designs and delivers Mobility-Defined Networks that empower IT departments and #GenMobile, a new generation of tech-savvy users who rely on their mobile devices for every aspect of work and personal communication. To create a mobility experience that #GenMobile and IT can rely upon, Aruba Mobility-Defined Networks™ automate infrastructure-wide performance optimization and trigger security actions that used to require manual IT intervention. The results are dramatically improved productivity and lower operational costs.
To learn more, visit Aruba at http://www.arubanetworks.com. For real-time news updates follow Aruba on Twitter (@ArubaNetworks) and Facebook (https://www.facebook.com/pages/Aruba-Networks), and for the latest technical discussions on mobility and Aruba products visit Airheads at thttp://community.arubanetworks.com.
Sign in with LinkedIn