Category: Artificial Intelligence

This post is co-written with Neel Patel, Abdullahi Olaoye, Kristopher Kersten, Aniket Deshpande from NVIDIA. Today, we’re excited to announce that the NVIDIA Evo-2 NVIDIA NIM microservice are now listed in Amazon SageMaker JumpStart. You can use this launch to deploy accelerated and specialized NIM microservices to build, experiment, and responsibly scale your drug discovery […]

Stay current with the latest serverless innovations that can transform your applications. In this 31st quarterly recap, discover the most impactful AWS serverless launches, features, and resources from Q4 2025 that you might have missed.

In healthcare, generative AI is transforming how medical professionals analyze data, summarize clinical notes, and generate insights to improve patient outcomes. From automating medical documentation to assisting in diagnostic reasoning, large language models (LLMs) have the potential to augment clinical workflows and accelerate research. However, these innovations also introduce significant privacy, security, and intellectual property challenges.

Organizations often have large volumes of documents containing valuable information that remains locked away and unsearchable. This solution addresses the need for a scalable, automated text extraction and knowledge base pipeline that transforms static document collections into intelligent, searchable repositories for generative AI applications.

Modern generative AI applications often need to stream large language model (LLM) outputs to users in real-time. Instead of waiting for a complete response, streaming delivers partial results as they become available, which significantly improves the user experience for chat interfaces and long-running AI tasks. This post compares three serverless approaches to handle Amazon Bedrock LLM streaming on Amazon Web Services (AWS), which helps you choose the best fit for your application.

Today, AWS announced support for response streaming in Amazon API Gateway to significantly improve the responsiveness of your REST APIs by progressively streaming response payloads back to the client. With this new capability, you can use streamed responses to enhance user experience when building LLM-driven applications (such as AI agents and chatbots), improve time-to-first-byte (TTFB) performance for web and mobile applications, stream large files, and perform long-running operations while reporting incremental progress using protocols such as server-sent events (SSE).

This two-part series explores the different architectural patterns, best practices, code implementations, and design considerations essential for successfully integrating generative AI solutions into both new and existing applications. In this post, we focus on patterns applicable for architecting real-time generative AI applications.

Imagine an AI assistant that doesn’t just respond to prompts – it reasons through goals, acts, and integrates with real-time systems. This is the promise of agentic AI. According to Gartner, by 2028 over 33% of enterprise applications will embed agentic capabilities – up from less than 1% today. While early generative AI efforts focused […]

Many organizations implement intelligent document processing pipelines in order to extract meaningful insights from an increasing volume of unstructured content (such as insurance claims, loan applications and more). Traditionally, these pipelines require significant engineering efforts, as the implementation often involves using several machine learning (ML) models and orchestrating complex workflows. As organizations integrate these pipelines […]

Efficient resource management is crucial for organizations seeking to optimize cloud costs while making sure of seamless access to compute capacity. Amazon EC2 On-Demand Capacity Reservations (ODCRs) provide the flexibility to reserve compute capacity within a specific Availability Zone (AZ) for any duration. In this post, we demonstrate how Amazon Bedrock Agents can help organizations gain actionable insights into ODCR usage across their AWS environment.