AWS Machine Learning Blog

Category: Amazon Machine Learning

Cost-effective document classification using the Amazon Titan Multimodal Embeddings Model

Organizations across industries want to categorize and extract insights from high volumes of documents of different formats. Manually processing these documents to classify and extract information remains expensive, error prone, and difficult to scale. Advances in generative artificial intelligence (AI) have given rise to intelligent document processing (IDP) solutions that can automate the document classification, […]

Knowledge Bases for Amazon Bedrock now supports custom prompts for the RetrieveAndGenerate API and configuration of the maximum number of retrieved results

With Knowledge Bases for Amazon Bedrock, you can securely connect foundation models (FMs) in Amazon Bedrock to your company data for Retrieval Augmented Generation (RAG). Access to additional data helps the model generate more relevant, context-specific, and accurate responses without retraining the FMs. In this post, we discuss two new features of Knowledge Bases for […]

Knowledge Bases for Amazon Bedrock now supports metadata filtering to improve retrieval accuracy

At AWS re:Invent 2023, we announced the general availability of Knowledge Bases for Amazon Bedrock. With Knowledge Bases for Amazon Bedrock, you can securely connect foundation models (FMs) in Amazon Bedrock to your company data using a fully managed Retrieval Augmented Generation (RAG) model. For RAG-based applications, the accuracy of the generated responses from FMs […]

Boost inference performance for Mixtral and Llama 2 models with new Amazon SageMaker containers

In January 2024, Amazon SageMaker launched a new version (0.26.0) of Large Model Inference (LMI) Deep Learning Containers (DLCs). This version offers support for new models (including Mixture of Experts), performance and usability improvements across inference backends, as well as new generation details for increased control and prediction explainability (such as reason for generation completion […]

Build a contextual text and image search engine for product recommendations using Amazon Bedrock and Amazon OpenSearch Serverless

In this post, we show how to build a contextual text and image search engine for product recommendations using the Amazon Titan Multimodal Embeddings model, available in Amazon Bedrock, with Amazon OpenSearch Serverless.

AWS and Mistral AI commit to democratizing generative AI with a strengthened collaboration

Today, Mistral AI is bringing its latest and most capable model, Mistral Large, to Amazon Bedrock, and is committed to making future models accessible to AWS customers. Mistral AI will also use AWS AI-optimized AWS Trainium and AWS Inferentia to build and deploy its future foundation models on Amazon Bedrock, benefitting from the price, performance, scale, and security of AWS. Along with this announcement, starting today, customers can use Amazon Bedrock in the AWS Europe (Paris) Region. At launch, customers will have access to some of the latest models from Amazon, Anthropic, Cohere, and Mistral AI, expanding their options to support various use cases from text understanding to complex reasoning.

Scale LLMs with PyTorch 2.0 FSDP on Amazon EKS – Part 2

This is a guest post co-written with Meta’s PyTorch team and is a continuation of Part 1 of this series, where we demonstrate the performance and ease of running PyTorch 2.0 on AWS. Machine learning (ML) research has proven that large language models (LLMs) trained with significantly large datasets result in better model quality. In […]

Provide live agent assistance for your chatbot users with Amazon Lex and Talkdesk cloud contact center

Amazon Lex provides advanced conversational artificial intelligence (AI) capabilities to enable self-service support for your organization’s contact center. With Amazon Lex, you can implement an omnichannel strategy where customers engage via phone, websites, and messaging platforms. The bots can answer FAQs, provide self-service experiences, or triage customer requests before transferring to a human agent. Amazon Lex integrates […]

Advanced RAG patterns on Amazon SageMaker

Today, customers of all industries—whether it’s financial services, healthcare and life sciences, travel and hospitality, media and entertainment, telecommunications, software as a service (SaaS), and even proprietary model providers—are using large language models (LLMs) to build applications like question and answering (QnA) chatbots, search engines, and knowledge bases. These generative AI applications are not only […]

Efficient continual pre-training LLMs for financial domains

Large language models (LLMs) are generally trained on large publicly available datasets that are domain agnostic. For example, Meta’s Llama models are trained on datasets such as CommonCrawl, C4, Wikipedia, and ArXiv. These datasets encompass a broad range of topics and domains. Although the resulting models yield amazingly good results for general tasks, such as […]