AWS Machine Learning Blog

In today’s data-intensive business landscape, organizations face the challenge of extracting valuable insights from diverse data sources scattered across their infrastructure. Whether it’s structured data in databases or unstructured content in document repositories, enterprises often struggle to efficiently query and use this wealth of information. In this post, we explore how you can use Amazon

In this post, we illustrate automating the responses to email inquiries by using Amazon Bedrock Knowledge Bases and Amazon Simple Email Service (Amazon SES), both fully managed services. By linking user queries to relevant company domain information, Amazon Bedrock Knowledge Bases offers personalized responses.

In this post, we explore an innovative approach that uses LLMs on Amazon Bedrock to intelligently extract metadata filters from natural language queries. By combining the capabilities of LLM function calling and Pydantic data models, you can dynamically extract metadata from user queries. This approach can also enhance the quality of retrieved information and responses generated by the RAG applications.

This post shows how Mark43 uses Amazon Q Business to create a secure, generative AI-powered assistant that drives operational efficiency and improves community service. We explain how they embedded Amazon Q Business web experience in their web application with low code, so they could focus on creating a rich AI experience for their customers.

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 training on Amazon SageMaker P5 instances.

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 an engineer transitioning from legacy networks to cloud technologies, I had never considered myself a developer.

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 and ML sessions. Visit the session catalog to learn about all our generative AI and ML sessions.

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 process, covering fine-tuning, deployment, and evaluation. We compare a customized SLM with a general purpose LLM, using various metrics to assess vocabulary richness and overall accuracy.

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.

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 and potentially harmful content, while maintaining safety and privacy. In this post, we explore a solution that automates building guardrails using a test-driven development approach.

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 you information on how you can get started.

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 alerts and recommendations, avoiding reactive escalations and other damages.

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 is powered by an intelligent agent that routes user questions to specialized tools that are optimized for different data types such as text, tables, and domain-specific formats. It uses the LLM’s ability to understand natural language, write code, and reason about conversational context.

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 SQL queries from natural language questions. The solution addresses challenges such as coded columns, non-intuitive names, and ambiguous queries, significantly reducing query time and democratizing data access.

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, managers, and other personas within your corporation to provide better visibility over maintaining a resilient architecture.

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 describe it, evaluate it with a score from 1–10, explain the reason behind the given score, and suggest improvements to the image.

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 confidently help our customers understand the policies without reaching out to insurance subject matter experts (SMEs) or memorizing complex plans while providing sales and after-sales services. The use of this solution has improved sales, cross-selling, and overall customer service experience.

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.

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, focusing specifically on solutions that handle both text and image data.

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 to unlock real value from their vast amounts of data that exist in image form. In this post, we discuss the benefits and capabilities of this new model with some examples.