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1. 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...

2. 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.

3. 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...

4. 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...

5. 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.

6. 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.

7. 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.

8. AWS AI League: Model customization and agentic showdown

   In this post, we explore the new AWS AI League challenges and how they are transforming how organizations approach AI development. The grand finale at AWS re:Invent 2025 was an exciting showcase of their ingenuity and skills.

9. Accelerate Enterprise AI Development using Weights & Biases and Amazon Bedrock AgentCore

   In this post, we demonstrate how to use Foundation Models (FMs) from Amazon Bedrock and the newly launched Amazon Bedrock AgentCore alongside W&B Weave to help build, evaluate, and monitor enterprise AI solutions. We cover the complete development lifecycle from tracking individual FM calls to monitoring complex agent workflows in production.

10. How dLocal automated compliance reviews using Amazon Quick Automate

    In this post, we share how dLocal worked closely with the AWS team to help shape the product roadmap, reinforce its role as an industry innovator, and set new benchmarks for operational excellence in the global fintech landscape.