Artificial Intelligence

In this post, we explore how Amazon Nova Multimodal Embeddings addresses the challenges of crossmodal search through a practical ecommerce use case. We examine the technical limitations of traditional approaches and demonstrate how Amazon Nova Multimodal Embeddings enables retrieval across text, images, and other modalities. You learn how to implement a crossmodal search system by generating embeddings, handling queries, and measuring performance. We provide working code examples and share how to add these capabilities to your applications.

Quantized models can be seamlessly deployed on Amazon SageMaker AI using a few lines of code. In this post, we explore why quantization matters—how it enables lower-cost inference, supports deployment on resource-constrained hardware, and reduces both the financial and environmental impact of modern LLMs, while preserving most of their original performance. We also take a deep dive into the principles behind PTQ and demonstrate how to quantize the model of your choice and deploy it on Amazon SageMaker.

Beekeeper’s automated leaderboard approach and human feedback loop system for dynamic LLM and prompt pair selection addresses the key challenges organizations face in navigating the rapidly evolving landscape of language models.

This post, developed through a strategic scientific partnership between AWS and the Instituto de Ciência e Tecnologia Itaú (ICTi), P&D hub maintained by Itaú Unibanco, the largest private bank in Latin America, explores the technical aspects of sentiment analysis for both text and audio. We present experiments comparing multiple machine learning (ML) models and services, discuss the trade-offs and pitfalls of each approach, and highlight how AWS services can be orchestrated to build robust, end-to-end solutions. We also offer insights into potential future directions, including more advanced prompt engineering for large language models (LLMs) and expanding the scope of audio-based analysis to capture emotional cues that text data alone might miss.

This post provides a detailed architectural overview of how TrueLook built its AI-powered safety monitoring system using SageMaker AI, highlighting key technical decisions, pipeline design patterns, and MLOps best practices. You will gain valuable insights into designing scalable computer vision solutions on AWS, particularly around model training workflows, automated pipeline creation, and production deployment strategies for real-time inference.

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.

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 redacted email communications and attachments. We walk through the step-by-step solution implementation procedures used to deploy this solution. Finally, we discuss the solution benefits, including operational efficiency, scalability, security and compliance, and adaptability.

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.

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 instructions to validate your migration’s success.

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