Category: Best Practices

Organizations are continuously seeking ways to use their proprietary knowledge and domain expertise to gain a competitive edge. With the advent of foundation models (FMs) and their remarkable natural language processing capabilities, a new opportunity has emerged to unlock the value of their data assets. As organizations strive to deliver personalized experiences to customers using […]

In this post, we explore a solution for implementing load balancing across login nodes in Slurm-based HyperPod clusters. By distributing user activity evenly across all available nodes, this approach provides more consistent performance, better resource utilization, and a smoother experience for all users. We guide you through the setup process, providing practical steps to achieve effective load balancing in your HyperPod clusters.

Today, we are excited to announce that Mistral-NeMo-Base-2407 and Mistral-NeMo-Instruct-2407 large language models from Mistral AI that excel at text generation, are available for customers through Amazon SageMaker JumpStart. In this post, we walk through how to discover, deploy and use the Mistral-NeMo-Instruct-2407 and Mistral-NeMo-Base-2407 models for a variety of real-world use cases.

Amazon Finance Automation developed a large language model (LLM)-based question-answer chat assistant on Amazon Bedrock. This solution empowers analysts to rapidly retrieve answers to customer queries, generating prompt responses within the same communication thread. As a result, it drastically reduces the time required to address customer queries. In this post, we share how Amazon Finance Automation built this generative AI Q&A chat assistant using Amazon Bedrock.

For enterprises in the realm of cloud computing and software development, providing secure code repositories is essential. As sophisticated cybersecurity threats become more prevalent, organizations must adopt proactive measures to protect their assets. Amazon Bedrock offers a powerful solution by automating the process of scanning repositories for vulnerabilities and remediating them. This post explores how you can use Amazon Bedrock to enhance the security of your repositories and maintain compliance with organizational and regulatory standards.

This post serves as a step-by-step guide on how to set up lifecycle configurations for your Amazon SageMaker Studio domains. With lifecycle configurations, system administrators can apply automated controls to their SageMaker Studio domains and their users. We cover core concepts of SageMaker Studio and provide code examples of how to apply lifecycle configuration to […]

This post dives deep into how to set up data governance at scale using Amazon DataZone for the data mesh. The data mesh is a modern approach to data management that decentralizes data ownership and treats data as a product. It enables different business units within an organization to create, share, and govern their own data assets, promoting self-service analytics and reducing the time required to convert data experiments into production-ready applications.

In this blog post, we demonstrate prompt engineering techniques to generate accurate and relevant analysis of tabular data using industry-specific language. This is done by providing large language models (LLMs) in-context sample data with features and labels in the prompt. The results are similar to fine-tuning LLMs without the complexities of fine-tuning models.

While organizations continue to discover the powerful applications of generative AI, adoption is often slowed down by team silos and bespoke workflows. To move faster, enterprises need robust operating models and a holistic approach that simplifies the generative AI lifecycle. In the first part of the series, we showed how AI administrators can build a […]

In this post, we discuss how to use AWS to support a decentralized clinical trial across the four main pillars of a decentralized clinical trial (virtual trials, personalized patient engagement, patient-centric trial design, and centralized data management). By exploring these AWS powered alternatives, we aim to demonstrate how organizations can drive progress towards more environmentally friendly clinical research practices.