Category: HAQM Simple Storage Service (S3)

This post outlines a custom multilingual document extraction and content assessment framework using a combination of Anthropic’s Claude 3 on HAQM Bedrock and HAQM A2I to incorporate human-in-the-loop capabilities.

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.

In this post, we demonstrate how to simplify data access to HAQM S3 from SageMaker Studio using S3 Access Grants, specifically for different user personas using IAM principals.

In this blog post, we show you how HAQM Rekognition can mitigate congestion at traffic intersections and reduce operations and maintenance costs.

In this post, we show you how to use HAQM Bedrock to automate email responses to customer queries. With our solution, you can identify the intent of customer emails and send an automated response if the intent matches your existing knowledge base or data sources. If the intent doesn’t have a match, the email goes to the support team for a manual response.

HAQM Bedrock is a fully managed service that offers a choice of high-performing foundation models (FMs) from leading artificial intelligence (AI) companies like AI21 Labs, Anthropic, Cohere, Meta, Stability AI, and HAQM through a single API, along with a broad set of capabilities to build generative AI applications with security, privacy, and responsible AI. With […]

In this post, we share the story of how The Weather Company (TWCo) enhanced its MLOps platform using services such as HAQM SageMaker, AWS CloudFormation, and HAQM CloudWatch. TWCo data scientists and ML engineers took advantage of automation, detailed experiment tracking, integrated training, and deployment pipelines to help scale MLOps effectively. TWCo reduced infrastructure management time by 90% while also reducing model deployment time by 20%.

Advancements in artificial intelligence (AI) and machine learning (ML) are revolutionizing the financial industry for use cases such as fraud detection, credit worthiness assessment, and trading strategy optimization. To develop models for such use cases, data scientists need access to various datasets like credit decision engines, customer transactions, risk appetite, and stress testing. Managing appropriate […]

February 9, 2024: HAQM Kinesis Data Firehose has been renamed to HAQM Data Firehose. Read the AWS What’s New post to learn more. At a basic level, Machine Learning (ML) technology learns from data to make predictions. Businesses use their data with an ML-powered personalization service to elevate their customer experience. This approach allows businesses […]

In this blog, you will learn to build a cloud-native FL architecture on AWS. By using infrastructure as code (IaC) tools on AWS, you can deploy FL architectures with ease. Also, a cloud-native architecture takes full advantage of a variety of AWS services with proven security and operational excellence, thereby simplifying the development of FL.