Category: Generative AI

As we enter 2025, OpenSearch Service support for OpenSearch 2.17 brings these improvements to the service. In this post, we walk through 2024’s innovations with an eye to how you can adopt new features to lower your cost, reduce your latency, and improve the accuracy of your search results and generated text.

This blog post introduces a new disk-based vector search approach that allows efficient querying of vectors stored on disk without loading them entirely into memory. By implementing these quantization methods, organizations can achieve compression ratios of up to 64x, enabling cost-effective scaling of vector databases for large-scale AI and machine learning applications.

HAQM Redshift made significant strides in 2024, that enhanced price-performance, enabled data lakehouse architectures by blurring the boundaries between data lakes and data warehouses, simplified ingestion and accelerated near real-time analytics, and incorporated generative AI capabilities to build natural language-based applications and boost user productivity. This blog post provides a comprehensive overview of the major product innovations and enhancements made to HAQM Redshift in 2024.

This post demonstrates how generative AI troubleshooting for Spark in AWS Glue helps your day-to-day Spark application debugging. It simplifies the debugging process for your Spark applications by using generative AI to automatically identify the root cause of failures and provides actionable recommendations to resolve the issues.

Today, we are excited to announce the preview of generative AI upgrades for Spark, a new capability that enables data practitioners to quickly upgrade and modernize their Spark applications running on AWS. Starting with Spark jobs in AWS Glue, this feature allows you to upgrade from an older AWS Glue version to AWS Glue version 4.0. This new capability reduces the time data engineers spend on modernizing their Spark applications, allowing them to focus on building new data pipelines and getting valuable analytics faster.

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 HAQM Bedrock and your data documentation.

In this post, we show you how to enable the HAQM 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 HAQM 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 HAQM Bedrock from streaming data in HAQM Managed Streaming for Apache Kafka (HAQM MSK) and indexing them in HAQM 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 HAQM MSK to simplify your RAG architecture.

HAQM 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 HAQM Bedrock. This integration enables you to use LLMs from simple SQL commands alongside your data in HAQM 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 […]