AWS Machine Learning Blog

Category: Compute

How Rocket Companies modernized their data science solution on AWS

In this post, we share how we modernized Rocket Companies’ data science solution on AWS to increase the speed to delivery from eight weeks to under one hour, improve operational stability and support by reducing incident tickets by over 99% in 18 months, power 10 million automated data science and AI decisions made daily, and provide a seamless data science development experience.

How Formula 1® uses generative AI to accelerate race-day issue resolution

In this post, we explain how F1 and AWS have developed a root cause analysis (RCA) assistant powered by Amazon Bedrock to reduce manual intervention and accelerate the resolution of recurrent operational issues during races from weeks to minutes. The RCA assistant enables the F1 team to spend more time on innovation and improving its services, ultimately delivering an exceptional experience for fans and partners. The successful collaboration between F1 and AWS showcases the transformative potential of generative AI in empowering teams to accomplish more in less time.

Virtual Meteorologist Featured Image

Building a virtual meteorologist using Amazon Bedrock Agents

In this post, we present a streamlined approach to deploying an AI-powered agent by combining Amazon Bedrock Agents and a foundation model (FM). We guide you through the process of configuring the agent and implementing the specific logic required for the virtual meteorologist to provide accurate weather-related responses.

Solution Overview

Amazon Q Business simplifies integration of enterprise knowledge bases at scale

In this post, we demonstrate how to build a knowledge base solution by integrating enterprise data with Amazon Q Business using Amazon S3. This approach helps organizations improve operational efficiency, reduce response times, and gain valuable insights from their historical data. The solution uses AWS security best practices to promote data protection while enabling teams to create a comprehensive knowledge base from various data sources.

Building the future of construction analytics: CONXAI’s AI inference on Amazon EKS

CONXAI Technology GmbH is pioneering the development of an advanced AI platform for the Architecture, Engineering, and Construction (AEC) industry. In this post, we dive deep into how CONXAI hosts the state-of-the-art OneFormer segmentation model on AWS using Amazon Simple Storage Service (Amazon S3), Amazon Elastic Kubernetes Service (Amazon EKS), KServe, and NVIDIA Triton.

The following diagram illustrates the workflow of patch-level prediction tasks on a WSI

Accelerate digital pathology slide annotation workflows on AWS using H-optimus-0

In this post, we demonstrate how to use H-optimus-0 for two common digital pathology tasks: patch-level analysis for detailed tissue examination, and slide-level analysis for broader diagnostic assessment. Through practical examples, we show you how to adapt this FM to these specific use cases while optimizing computational resources.

Aetion Services

How Aetion is using generative AI and Amazon Bedrock to unlock hidden insights about patient populations

In this post, we review how Aetion’s Smart Subgroups Interpreter enables users to interact with Smart Subgroups using natural language queries. Powered by Amazon Bedrock and Anthropic’s Claude 3 large language models (LLMs), the interpreter responds to user questions expressed in conversational language about patient subgroups and provides insights to generate further hypotheses and evidence.

Streamline custom environment provisioning for Amazon SageMaker Studio: An automated CI/CD pipeline approach

In this post, we show how to create an automated continuous integration and delivery (CI/CD) pipeline solution to build, scan, and deploy custom Docker images to SageMaker Studio domains. You can use this solution to promote consistency of the analytical environments for data science teams across your enterprise.

Implement RAG while meeting data residency requirements using AWS hybrid and edge services

In this post, we show how to extend Amazon Bedrock Agents to hybrid and edge services such as AWS Outposts and AWS Local Zones to build distributed Retrieval Augmented Generation (RAG) applications with on-premises data for improved model outcomes. With Outposts, we also cover a reference pattern for a fully local RAG application that requires both the foundation model (FM) and data sources to reside on premises.

Embodied AI Chess with Amazon Bedrock

In this post, we demonstrate Embodied AI Chess with Amazon Bedrock, bringing a new dimension to traditional chess through generative AI capabilities. Our setup features a smart chess board that can detect moves in real time, paired with two robotic arms executing those moves. Each arm is controlled by different FMs—base or custom. This physical implementation allows you to observe and experiment with how different generative AI models approach complex gaming strategies in real-world chess matches.