Category: Analytics

This post dives deep into how to set up data governance at scale using HAQM DataZone for the data mesh. The data mesh is a modern approach to data management that decentralizes data ownership and treats data as a product. It enables different business units within an organization to create, share, and govern their own data assets, promoting self-service analytics and reducing the time required to convert data experiments into production-ready applications.

In this post, we demonstrate how to create an automated email response solution using HAQM Bedrock and its features, including HAQM Bedrock Agents, HAQM Bedrock Knowledge Bases, and HAQM Bedrock Guardrails.

Today, we are happy to announce the availability of Binary Embeddings for HAQM Titan Text Embeddings V2 in HAQM Bedrock Knowledge Bases and HAQM OpenSearch Serverless. This post summarizes the benefits of this new binary vector support and gives you information on how you can get started.

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 HAQM Bedrock. HAQM Bedrock is a fully managed service that makes foundation models (FMs) from leading AI startups and HAQM available through an API, so you can choose from a wide range of FMs to find the model that is best suited for your use case. With HAQM Bedrock, GoDaddy’s Lighthouse mines insights from customer care interactions using crafted prompts to identify top call drivers and reduce friction points in customers’ product and website experiences, leading to improved customer experience.

This post explores a solution that uses the power of AWS generative AI capabilities like HAQM Bedrock and OpenSearch vector search to perform damage appraisals for insurers, repair shops, and fleet managers.

In this post, you will learn how to extract key objects from image queries using HAQM Rekognition and build a reverse image search engine using HAQM Titan Multimodal Embeddings from HAQM Bedrock in combination with HAQM OpenSearch Serverless Service.

This post presents an architectural approach to extract data from different cloud environments, such as Google Cloud Platform (GCP) BigQuery, without the need for data movement. This minimizes the complexity and overhead associated with moving data between cloud environments, enabling organizations to access and utilize their disparate data assets for ML projects. We highlight the process of using HAQM Athena Federated Query to extract data from GCP BigQuery, using HAQM SageMaker Data Wrangler to perform data preparation, and then using the prepared data to build ML models within HAQM SageMaker Canvas, a no-code ML interface.

In this post, we will explore building a reusable RAG data pipeline on LangChain—an open source framework for building applications based on LLMs—and integrating it with AWS Glue and HAQM OpenSearch Serverless. The end solution is a reference architecture for scalable RAG indexing and deployment.

In this post, we dive into a business use case for a banking institution. We will show you how a financial or business analyst at a bank can easily predict if a customer’s loan will be fully paid, charged off, or current using a machine learning model that is best for the business problem at hand.

Agentic workflows are a fresh new perspective in building dynamic and complex business use- case based workflows with the help of large language models (LLM) as their reasoning engine or brain. In this post, we set up an agent using HAQM Bedrock Agents to act as a software application builder assistant.