Category: Intermediate (200)

In this post, you will learn five practical patterns for building resilient generative AI applications on AWS, progressing from native Amazon Bedrock features to multi-model orchestration using an LLM gateway. These patterns address real-world challenges such as quota exhaustion during unexpected traffic surges, maximizing availability through geographic distribution of inference, and helping prevent noisy neighbor problems in multi-tenant environments.

In this post, we demonstrate how to implement video upscaling using SeedVR2 on SageMaker AI. We cover the solution architecture, walk through the deployment steps, and show performance comparisons that highlight the quality improvements and processing efficiency you can achieve. By the end of this post, you’ll have the practical knowledge needed to implement this super resolution solution.

This post shows you how to build a conversational protein research assistant that combines three capabilities: Natural language query parsing to extract structured search parameters, vector similarity search over protein embeddings using a specialized language model and ai-generated scientific summaries of search results.

In this post, we walk you through how to deploy ComfyUI workflows on Amazon SageMaker AI processing jobs to generate hundreds of high-quality images in a single batch. You learn how to set up the infrastructure using AWS Cloud Development Kit (AWS CDK), configure GPU-accelerated processing, and automate image generation at scale. You can then adapt this solution to your ComfyUI workflows specific to your needs. We will guide you through a practical, step-by-step process to automate ComfyUI workflows to generate hundreds of high-quality images in a single batch empowering you to scale your creative pipeline.

Amazon SageMaker AI provides fully managed real-time inference hosting for machine learning models. You deploy a model to a SageMaker endpoint backed by one or more compute instances, and SageMaker handles provisioning and scaling. SageMaker supports multiple endpoint architectures. This post focuses on the two most relevant to generative AI workloads with detailed observability: Single-model endpoints (SME) and Inference component (IC) endpoints.

Today, we’re announcing inline payload support for Amazon SageMaker AI Async Inference. Customers can now send inference payloads directly in the request body of the InvokeEndpointAsync API, removing the need to upload input data to Amazon Simple Storage Service (Amazon S3) before each invocation.

Today, we’re announcing a new API with Amazon Bedrock Guardrails. With this API, you can apply individual safeguards, also referred to as safety checks, at any point in your agentic AI applications without creating guardrail resources. In this post, we walk through how the InvokeGuardrailChecks API works and how to use it to build safe, multi-turn agentic AI applications.

Today, we’re excited to announce container image caching for Amazon SageMaker AI inference, the next major advancement in our faster scaling optimization journey. This speeds up end-to-end latency by up to 2x for generative AI models during scale-out events.

In this post, we walk you through calling the detector functions to diagnose real agent failures. You learn how to interpret their structured output: categorized failures with confidence scores, causal chains linking root causes to downstream symptoms, and fix recommendations specifying whether a change belongs in your system prompt or tool definitions. You also learn how to integrate detection into your evaluation pipeline for automated diagnosis on every test run.

In this post, you’ll build a competitive research agent that demonstrates this pattern end to end. This walkthrough targets developers building multi-step AI workflows who need isolated execution environments for their agents. In Part 2 of the notebook, you can deploy this same agent to Bedrock AgentCore Runtime using the AgentCore CLI, so it runs as a managed, session-isolated service.