AWS Machine Learning Blog
How FP8 boosts LLM training by 18% on Amazon SageM...
LLM training has seen remarkable advances in recent years, with organizations pushing the boundaries of what’s possible in terms of model size, performance, and efficiency. In this post, we explore how FP8 optimization can significantly speed up large model...
Racing into the future: How AWS DeepRacer fueled m...
In 2018, I sat in the audience at AWS re:Invent as Andy Jassy announced AWS DeepRacer—a fully autonomous 1/18th scale race car driven by reinforcement learning. At the time, I knew little about AI or machine learning (ML). As...
Your guide to generative AI and ML at AWS re:Inven...
In this attendee guide, we’re highlighting a few of our favorite sessions to give you a glimpse into what’s in store. To help you plan your agenda for this year’s re:Invent, here are some highlights of the generative AI...
Customize small language models on AWS with automo...
In this post, we guide you through the phases of customizing SLMs on AWS, with a specific focus on automotive terminology for diagnostics as a Q&A task. We begin with the data analysis phase and progress through the end-to-end...
Automate emails for task management using Amazon B...
In this post, we demonstrate how to create an automated email response solution using Amazon Bedrock and its features, including Amazon Bedrock Agents, Amazon Bedrock Knowledge Bases, and Amazon Bedrock Guardrails.
Automate building guardrails for Amazon Bedrock us...
Amazon Bedrock Guardrails helps implement safeguards for generative AI applications based on specific use cases and responsible AI policies. Amazon Bedrock Guardrails assists in controlling the interaction between users and foundation models (FMs) by detecting and filtering out undesirable...
Build cost-effective RAG applications with Binary ...
Today, we are happy to announce the availability of Binary Embeddings for Amazon Titan Text Embeddings V2 in Amazon Bedrock Knowledge Bases and Amazon OpenSearch Serverless. This post summarizes the benefits of this new binary vector support and gives...
Automate cloud security vulnerability assessment a...
This post demonstrates a proactive approach for security vulnerability assessment of your accounts and workloads, using Amazon GuardDuty, Amazon Bedrock, and other AWS serverless technologies. This approach aims to identify potential vulnerabilities proactively and provide your users with timely...
DXC transforms data exploration for their oil and ...
In this post, we show you how DXC and AWS collaborated to build an AI assistant using large language models (LLMs), enabling users to access and analyze different data types from a variety of data sources. The AI assistant...
How MSD uses Amazon Bedrock to translate natural l...
MSD, a leading pharmaceutical company, collaborates with AWS to implement a powerful text-to-SQL generative AI solution using Amazon Bedrock and Anthropic's Claude 3.5 Sonnet model. This approach streamlines data extraction from complex healthcare databases like DE-SynPUF, enabling analysts to...
Generate AWS Resilience Hub findings in natural la...
This blog post discusses a solution that combines AWS Resilience Hub and Amazon Bedrock to generate architectural findings in natural language. By using the capabilities of Resilience Hub and Amazon Bedrock, you can share findings with C-suite executives, engineers,...
Generate and evaluate images in Amazon Bedrock wit...
In this post, we demonstrate how to interact with the Amazon Titan Image Generator G1 v2 model on Amazon Bedrock to generate an image. Then, we show you how to use Anthropic’s Claude 3.5 Sonnet on Amazon Bedrock to...
How InsuranceDekho transformed insurance agent int...
In this post, we explain how InsuranceDekho harnessed the power of generative AI using Amazon Bedrock and Anthropic’s Claude to provide responses to customer queries on policy coverages, exclusions, and more. This let our customer care agents and POSPs...
Considerations for addressing the core dimensions ...
In this post, we introduce the core dimensions of responsible AI and explore considerations and strategies on how to address these dimensions for Amazon Bedrock applications.
From RAG to fabric: Lessons learned from building ...
This post focuses on doing RAG on heterogeneous data formats. We first introduce routers, and how they can help managing diverse data sources. We then give tips on how to handle tabular data and will conclude with multimodal RAG,...
Cohere Embed multimodal embeddings model is now av...
The Cohere Embed multimodal embeddings model is now generally available on Amazon SageMaker JumpStart. This model is the newest Cohere Embed 3 model, which is now multimodal and capable of generating embeddings from both text and images, enabling enterprises...
How GoDaddy built Lighthouse, an interaction analy...
In this post, we discuss how GoDaddy’s Care & Services team, in close collaboration with the AWS GenAI Labs team, built Lighthouse—a generative AI solution powered by Amazon Bedrock. Amazon Bedrock is a fully managed service that makes foundation...
Fine-tune multimodal models for vision and text us...
In this post, we showcase how to fine-tune a text and vision model, such as Meta Llama 3.2, to better perform at visual question answering tasks. The Meta Llama 3.2 Vision Instruct models demonstrated impressive performance on the challenging...