Category: Generative AI

By harnessing the capabilities of generative AI, you can automate the generation of comprehensive metadata descriptions for your data assets based on their documentation, enhancing discoverability, understanding, and the overall data governance within your AWS Cloud environment. This post shows you how to enrich your AWS Glue Data Catalog with dynamic metadata using foundation models (FMs) on Amazon Bedrock and your data documentation.

In this post, we show you how to enable the Amazon Q generative SQL feature in the Redshift query editor and use the feature to get tailored SQL commands based on your natural language queries. With Amazon Q, you can spend less time worrying about the nuances of SQL syntax and optimizations, allowing you to concentrate your efforts on extracting invaluable business insights from your data.

We’re introducing a real-time vector embedding blueprint, which simplifies building real-time AI applications by automatically generating vector embeddings using Amazon Bedrock from streaming data in Amazon Managed Streaming for Apache Kafka (Amazon MSK) and indexing them in Amazon OpenSearch Service. In this post, we discuss the importance of real-time data for generative AI applications, typical architectural patterns for building Retrieval Augmented Generation (RAG) capabilities, and how to use real-time vector embedding blueprints for Amazon MSK to simplify your RAG architecture.

Amazon Redshift has enhanced its Redshift ML feature to support integration of large language models (LLMs). As part of these enhancements, Redshift now enables native integration with Amazon Bedrock. This integration enables you to use LLMs from simple SQL commands alongside your data in Amazon Redshift, helping you to build generative AI applications quickly. This powerful combination enables customers to harness the transformative capabilities of LLMs and seamlessly incorporate them into their analytical workflows.

While the potential of generative artificial intelligence (AI) is increasingly under evaluation, organizations are at different stages in defining their generative AI vision. In many organizations, the focus is on large language models (LLMs), and foundation models (FMs) more broadly. This is just the tip of the iceberg, because what enables you to obtain differential […]

In this blog post, we will highlight how ZS Associates used multiple AWS services to build a highly scalable, highly performant, clinical document search platform. This platform is an advanced information retrieval system engineered to assist healthcare professionals and researchers in navigating vast repositories of medical documents, medical literature, research articles, clinical guidelines, protocol documents, […]

Amazon Redshift ML is a feature of Amazon Redshift that enables you to build, train, and deploy machine learning (ML) models directly within the Redshift environment. Now, you can use pretrained publicly available large language models (LLMs) in Amazon SageMaker JumpStart as part of Redshift ML, allowing you to bring the power of LLMs to analytics. You can use pretrained publicly available LLMs from leading providers such as Meta, AI21 Labs, LightOn, Hugging Face, Amazon Alexa, and Cohere as part of your Redshift ML workflows. By integrating with LLMs, Redshift ML can support a wide variety of natural language processing (NLP) use cases on your analytical data, such as text summarization, sentiment analysis, named entity recognition, text generation, language translation, data standardization, data enrichment, and more. Through this feature, the power of generative artificial intelligence (AI) and LLMs is made available to you as simple SQL functions that you can apply on your datasets. The integration is designed to be simple to use and flexible to configure, allowing you to take advantage of the capabilities of advanced ML models within your Redshift data warehouse environment.

Data streaming enables generative AI to take advantage of real-time data and provide businesses with rapid insights. This post looks at how to integrate generative AI capabilities when implementing a streaming architecture on AWS using managed services such as Managed Service for Apache Flink and Amazon Kinesis Data Streams for processing streaming data and Amazon Bedrock to utilize generative AI capabilities. We include a reference architecture and a step-by-step guide on infrastructure setup and sample code for implementing the solution with the AWS Cloud Development Kit (AWS CDK). You can find the code to try it out yourself on the GitHub repo.

This post takes a step-by-step approach to showcase how you can use Retrieval Augmented Generation (RAG) to reference real-time tweets as a context for large language models (LLMs). RAG is the process of optimizing the output of an LLM so it references an authoritative knowledge base outside of its training data sources before generating a response. LLMs are trained on vast volumes of data and use billions of parameters to generate original output for tasks such as answering questions, translating languages, and completing sentences.

In March 2024, we announced the general availability of the generative artificial intelligence (AI) generated data descriptions in Amazon DataZone. In this post, we share what we heard from our customers that led us to add the AI-generated data descriptions and discuss specific customer use cases addressed by this capability. We also detail how the […]