Tag: AI/ML

This post shows you how to implement robust error handling strategies that can help improve application reliability and user experience when using Amazon Bedrock. We’ll dive deep into strategies for optimizing performances for the application with these errors. Whether this is for a fairly new application or matured AI application, in this post you will be able to find the practical guidelines to operate with on these errors.

In this post, we discuss how Amazon Nova in Amazon Bedrock can be used to implement an AI-powered image recognition solution that automates the detection and validation of module components, significantly reducing manual verification efforts and improving accuracy.

In this post, you will learn how to deploy Fullstack AgentCore Solution Template (FAST) to your Amazon Web Services (AWS) account, understand its architecture, and see how to extend it for your requirements. You will learn how to build your own agent while FAST handles authentication, infrastructure as code (IaC), deployment pipelines, and service integration.

In this post, we explore the Amazon Nova rubric-based judge feature: what a rubric-based judge is, how the judge is trained, what metrics to consider, and how to calibrate the judge. We chare notebook code of the Amazon Nova rubric-based LLM-as-a-judge methodology to evaluate and compare the outputs of two different LLMs using SageMaker training jobs.

In this post, you will learn how to configure and use Amazon Nova Multimodal Embeddings for media asset search systems, product discovery experiences, and document retrieval applications.

This blog post explains how TR’s Platform Engineering team, a geographically distributed unit overseeing TR’s service availability, boosted its operational productivity by transitioning from manual to an automated agentic system using Amazon Bedrock AgentCore.

In this post, we explore how to build a multi-agent video processing workflow using Strands Agents, Meta’s Llama 4 models, and Amazon Bedrock to automatically analyze and understand video content through specialized AI agents working in coordination. To showcase the solution, we will use Amazon SageMaker AI to walk you through the code.

In this post, we explain how we overcame the limitations of single-agent AI systems through a human-centric approach, implemented structured outputs to significantly reduce hallucinations and built a scalable solution now positioned for expansion across the AMET QA team and later across other QA teams in International Emerging Stores and Payments (IESP) Org.

This post introduces generative AI guided business reporting—with a focus on writing achievements & challenges about your business—providing a smart, practical solution that helps simplify and accelerate internal communication and reporting.

In this post, we demonstrate how you can address these challenges by adding centralized safeguards to a custom multi-provider generative AI gateway using Amazon Bedrock Guardrails.