Category: Storage

In this post, we will walk through the performance constraints and design choices by OARC and REMAP teams at UCLA, including how AWS serverless infrastructure, AWS Managed Services, and generative AI services supported the rapid design and deployment of our solution. We will also describe our use of Amazon SageMaker AI and how it can be used reliably in immersive live experiences.

In this blog post, we show you how agentic workflows can accelerate the processing and interpretation of genomics pipelines at scale with a natural language interface. We demonstrate a comprehensive genomic variant interpreter agent that combines automated data processing with intelligent analysis to address the entire workflow from raw VCF file ingestion to conversational query interfaces.

In the post Principal Financial Group increases Voice Virtual Assistant performance using Genesys, Amazon Lex, and Amazon QuickSight, we discussed the overall Principal Virtual Assistant solution using Genesys Cloud, Amazon Lex V2, multiple AWS services, and a custom reporting and analytics solution using Amazon QuickSight.

In this post, we show how Splash Music is setting a new standard for AI-powered music creation by using its advanced HummingLM model with AWS Trainium on Amazon SageMaker HyperPod. As a selected startup in the 2024 AWS Generative AI Accelerator, Splash Music collaborated closely with AWS Startups and the AWS Generative AI Innovation Center (GenAIIC) to fast-track innovation and accelerate their music generation FM development lifecycle.

In this post, we demonstrate how to build an AI assistant using Amazon Q Business that responds to user requests based on your enterprise documents stored in an S3 bucket, and how the users can use the reference URLs in the AI assistant responses to view or download the referred documents, and verify the AI responses to practice responsible AI.

Today, I’m excited to share how we’re bringing this vision to life with new capabilities that address the fundamental aspects of building and deploying agents at scale. These innovations will help you move beyond experiments to production-ready agent systems that can be trusted with your most critical business processes.

In this post, we explore how to build a secure RAG application using serverless data lake architecture, an important data strategy to support generative AI development. We use Amazon Web Services (AWS) services including Amazon S3, Amazon DynamoDB, AWS Lambda, and Amazon Bedrock Knowledge Bases to create a comprehensive solution supporting unstructured data assets which can be extended to structured data. The post covers how to implement fine-grained access controls for your enterprise data and design metadata-driven retrieval systems that respect security boundaries. These approaches will help you maximize the value of your organization’s data while maintaining robust security and compliance.

In the fashion industry, teams are frequently innovating quickly, often utilizing AI. Sharing content, whether it be through videos, designs, or otherwise, can lead to content moderation challenges. There remains a risk (through intentional or unintentional actions) of inappropriate, offensive, or toxic content being produced and shared. In this post, we cover the use of the multimodal toxicity detection feature of Amazon Bedrock Guardrails to guard against toxic content. Whether you’re an enterprise giant in the fashion industry or an up-and-coming brand, you can use this solution to screen potentially harmful content before it impacts your brand’s reputation and ethical standards. For the purposes of this post, ethical standards refer to toxic, disrespectful, or harmful content and images that could be created by fashion designers.

In this post, we demonstrate how to use SageMaker AI to apply the Random Cut Forest (RCF) algorithm to detect anomalies in spacecraft position, velocity, and quaternion orientation data from NASA and Blue Origin’s demonstration of lunar Deorbit, Descent, and Landing Sensors (BODDL-TP).

The Institute of Science Tokyo has successfully trained Llama 3.3 Swallow, a 70-billion-parameter large language model (LLM) with enhanced Japanese capabilities, using Amazon SageMaker HyperPod. The model demonstrates superior performance in Japanese language tasks, outperforming GPT-4o-mini and other leading models. This technical report details the training infrastructure, optimizations, and best practices developed during the project.