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1. Scaling medical content review at Flo Health using Amazon Bedrock (Part 1)

   This two-part series explores Flo Health's journey with generative AI for medical content verification. Part 1 examines our proof of concept (PoC), including the initial solution, capabilities, and early results. Part 2 covers focusing on scaling challenges and real-world implementation. Each article stands alone while collectively showing how AI transforms medical content management at scale.

2. Detect and redact personally identifiable information using Amazon Bedrock Data Automation and Guardrails

   This post shows an automated PII detection and redaction solution using Amazon Bedrock Data Automation and Amazon Bedrock Guardrails through a use case of processing text and image content in high volumes of incoming emails and attachments. The solution features a complete email processing workflow with a React-based user interface for authorized personnel to more securely manage and review...

3. Speed meets scale: Load testing SageMakerAI endpoints with Observe.AI’s testing tool

   Observe.ai developed the One Load Audit Framework (OLAF), which integrates with SageMaker to identify bottlenecks and performance issues in ML services, offering latency and throughput measurements under both static and dynamic data loads. In this blog post, you will learn how to use the OLAF utility to test and validate your SageMaker endpoint.

4. Migrate MLflow tracking servers to Amazon SageMaker AI with serverless MLflow

   This post shows you how to migrate your self-managed MLflow tracking server to a MLflow App – a serverless tracking server on SageMaker AI that automatically scales resources based on demand while removing server patching and storage management tasks at no cost. Learn how to use the MLflow Export Import tool to transfer your experiments, runs, models, and other MLflow resources, including...

5. Build an AI-powered website assistant with Amazon Bedrock

   This post demonstrates how to solve this challenge by building an AI-powered website assistant using Amazon Bedrock and Amazon Bedrock Knowledge Bases.

6. Programmatically creating an IDP solution with Amazon Bedrock Data Automation

   In this post, we explore how to programmatically create an IDP solution that uses Strands SDK, Amazon Bedrock AgentCore, Amazon Bedrock Knowledge Base, and Bedrock Data Automation (BDA). This solution is provided through a Jupyter notebook that enables users to upload multi-modal business documents and extract insights using BDA as a parser to retrieve relevant chunks and...

7. AI agent-driven browser automation for enterprise workflow management

   Enterprise organizations increasingly rely on web-based applications for critical business processes, yet many workflows remain manually intensive, creating operational inefficiencies and compliance risks. Despite significant technology investments, knowledge workers routinely navigate between eight to twelve different web applications during standard workflows, constantly switching contexts and...

8. Agentic QA automation using Amazon Bedrock AgentCore Browser and Amazon Nova Act

   In this post, we explore how agentic QA automation addresses these challenges and walk through a practical example using Amazon Bedrock AgentCore Browser and Amazon Nova Act to automate testing for a sample retail application.

9. Optimizing LLM inference on Amazon SageMaker AI with BentoML’s LLM- Optimizer

   In this post, we demonstrate how to optimize large language model (LLM) inference on Amazon SageMaker AI using BentoML's LLM-Optimizer to systematically identify the best serving configurations for your workload.

10. Exploring the zero operator access design of Mantle

    In this post, we explore how Mantle, Amazon's next-generation inference engine for Amazon Bedrock, implements a zero operator access (ZOA) design that eliminates any technical means for AWS operators to access customer data.