Artificial Intelligence
Category: Advanced (300)
Responsible AI: How PowerSchool safeguards millions of students with AI-powered content filtering using Amazon SageMaker AI
In this post, we demonstrate how PowerSchool built and deployed a custom content filtering solution using Amazon SageMaker AI that achieved better accuracy while maintaining low false positive rates. We walk through our technical approach to fine tuning Llama 3.1 8B, our deployment architecture, and the performance results from internal validations.
Unlock global AI inference scalability using new global cross-Region inference on Amazon Bedrock with Anthropic’s Claude Sonnet 4.5
Organizations are increasingly integrating generative AI capabilities into their applications to enhance customer experiences, streamline operations, and drive innovation. As generative AI workloads continue to grow in scale and importance, organizations face new challenges in maintaining consistent performance, reliability, and availability of their AI-powered applications. Customers are looking to scale their AI inference workloads across […]
Accelerate development with the Amazon Bedrock AgentCore MCP server
Today, we’re excited to announce the Amazon Bedrock AgentCore Model Context Protocol (MCP) Server. With built-in support for runtime, gateway integration, identity management, and agent memory, the AgentCore MCP Server is purpose-built to speed up creation of components compatible with Bedrock AgentCore. You can use the AgentCore MCP server for rapid prototyping, production AI solutions, […]
Modernize fraud prevention: GraphStorm v0.5 for real-time inference
In this post, we demonstrate how to implement real-time fraud prevention using GraphStorm v0.5’s new capabilities for deploying graph neural network (GNN) models through Amazon SageMaker. We show how to transition from model training to production-ready inference endpoints with minimal operational overhead, enabling sub-second fraud detection on transaction graphs with billions of nodes and edges.
Building health care agents using Amazon Bedrock AgentCore
In this solution, we demonstrate how the user (a parent) can interact with a Strands or LangGraph agent in conversational style and get information about the immunization history and schedule of their child, inquire about the available slots, and book appointments. With some changes, AI agents can be made event-driven so that they can automatically send reminders, book appointments, and so on.
How Skello uses Amazon Bedrock to query data in a multi-tenant environment while keeping logical boundaries
Skello is a leading human resources (HR) software as a service (SaaS) solution focusing on employee scheduling and workforce management. Catering to diverse sectors such as hospitality, retail, healthcare, construction, and industry, Skello offers features including schedule creation, time tracking, and payroll preparation. We dive deep into the challenges of implementing large language models (LLMs) for data querying, particularly in the context of a French company operating under the General Data Protection Regulation (GDPR).
Create a private workforce on Amazon SageMaker Ground Truth with the AWS CDK
In this post, we present a complete solution for programmatically creating private workforces on Amazon SageMaker AI using the AWS Cloud Development Kit (AWS CDK), including the setup of a dedicated, fully configured Amazon Cognito user pool.
Build a serverless Amazon Bedrock batch job orchestration workflow using AWS Step Functions
In this post, we introduce a flexible and scalable solution that simplifies the batch inference workflow. This solution provides a highly scalable approach to managing your FM batch inference needs, such as generating embeddings for millions of documents or running custom evaluation or completion tasks with large datasets.
Document intelligence evolved: Building and evaluating KIE solutions that scale
In this blog post, we demonstrate an end-to-end approach for building and evaluating a KIE solution using Amazon Nova models available through Amazon Bedrock. This end-to-end approach encompasses three critical phases: data readiness (understanding and preparing your documents), solution development (implementing extraction logic with appropriate models), and performance measurement (evaluating accuracy, efficiency, and cost-effectiveness). We illustrate this comprehensive approach using the FATURA dataset—a collection of diverse invoice documents that serves as a representative proxy for real-world enterprise data.
Empowering air quality research with secure, ML-driven predictive analytics
In this post, we provide a data imputation solution using Amazon SageMaker AI, AWS Lambda, and AWS Step Functions. This solution is designed for environmental analysts, public health officials, and business intelligence professionals who need reliable PM2.5 data for trend analysis, reporting, and decision-making. We sourced our sample training dataset from openAFRICA. Our solution predicts PM2.5 values using time-series forecasting.