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Amazon Nova Multimodal Embeddings processes text, documents, images, video, and audio through a single model architecture. Available through Amazon Bedrock, the model converts different input modalities into numerical embeddings within the same vector space, supporting direct similarity calculations regardless of content type. We developed this unified model to reduce the need for separate embedding models, …

Crossmodal search with Amazon Nova Multimodal Embeddings Read More »

Foundation models (FMs) and large language models (LLMs) have been rapidly scaling, often doubling in parameter count within months, leading to significant improvements in language understanding and generative capabilities. This rapid growth comes with steep costs: inference now requires enormous memory capacity, high-performance GPUs, and substantial energy consumption. This trend is evident in the open …

Accelerating LLM inference with post-training weight and activation using AWQ and GPTQ on Amazon SageMaker AI Read More »

This post is cowritten by Mike Koźmiński from Beekeeper. Large Language Models (LLMs) are evolving rapidly, making it difficult for organizations to select the best model for each specific use case, optimize prompts for quality and cost, adapt to changing model capabilities, and personalize responses for different users. Choosing the “right” LLM and prompt isn’t …

How Beekeeper optimized user personalization with Amazon Bedrock Read More »

This post is co-written by Instituto de Ciência e Tecnologia Itaú (ICTi) and AWS. Sentiment analysis has grown increasingly important in modern enterprises, providing insights into customer opinions, satisfaction levels, and potential frustrations. As interactions occur largely through text (such as social media, chat applications, and ecommerce reviews) or voice (such as call centers and …

Sentiment Analysis with Text and Audio Using AWS Generative AI Services: Approaches, Challenges, and Solutions Read More »

This post is co-written by TrueLook and AWS. TrueLook is a construction camera and jobsite intelligence company that provides real-time visibility into construction projects. Its platform combines high-resolution time-lapse cameras, live video streaming, and AI-powered insights to help teams monitor progress, improve accountability, and reduce risk across the entire project lifecycle. TrueLook used Amazon SageMaker …

Architecting TrueLook’s AI-powered construction safety system on Amazon SageMaker AI Read More »

This blog post is based on work co-developed with Flo Health. Healthcare science is rapidly advancing. Maintaining accurate and up-to-date medical content directly impacts people’s lives, health decisions, and well-being. When someone searches for health information, they are often at their most vulnerable, making accuracy not just important, but potentially life-saving. Flo Health creates thousands …

Scaling medical content review at Flo Health using Amazon Bedrock (Part 1) Read More »

Organizations handle vast amounts of sensitive customer information through various communication channels. Protecting Personally Identifiable Information (PII), such as social security numbers (SSNs), driver’s license numbers, and phone numbers has become increasingly critical for maintaining compliance with data privacy regulations and building customer trust. However, manually reviewing and redacting PII is time-consuming, error-prone, and scales …

Detect and redact personally identifiable information using Amazon Bedrock Data Automation and Guardrails Read More »

This post is cowritten with Aashraya Sachdeva from Observe.ai. You can use Amazon SageMaker to build, train and deploy machine learning (ML) models, including large language models (LLMs) and other foundation models (FMs). This helps you significantly reduce the time required for a range of generative AI and ML development tasks. An AI/ML development cycle …

Speed meets scale: Load testing SageMakerAI endpoints with Observe.AI’s testing tool Read More »

Here are Google’s latest AI updates from December 2025 ​ 

Operating a self-managed MLflow tracking server comes with administrative overhead, including server maintenance and resource scaling. As teams scale their ML experimentation, efficiently managing resources during peak usage and idle periods is a challenge. Organizations running MLflow on Amazon EC2 or on-premises can optimize costs and engineering resources by using Amazon SageMaker AI with serverless …

Migrate MLflow tracking servers to Amazon SageMaker AI with serverless MLflow Read More »