Category: HAQM Bedrock

This post begins a blog series exploring DeepSeek and open FMs on HAQM Bedrock Custom Model Import. It covers the process of performance benchmarking of custom models in HAQM Bedrock using popular open source tools: LLMPerf and LiteLLM. It includes a notebook that includes step-by-step instructions to deploy a DeepSeek-R1-Distill-Llama-8B model, but the same steps apply for any other model supported by HAQM Bedrock Custom Model Import.

The integration of generative AI agents into business processes is poised to accelerate as organizations recognize the untapped potential of these technologies. Advancements in multimodal artificial intelligence (AI), where agents can understand and generate not just text but also images, audio, and video, will further broaden their applications. This post will discuss agentic AI driven architecture and ways of implementing.

In this post, we dive deeper into one of MaestroQA’s key features—conversation analytics, which helps support teams uncover customer concerns, address points of friction, adapt support workflows, and identify areas for coaching through the use of HAQM Bedrock. We discuss the unique challenges MaestroQA overcame and how they use AWS to build new features, drive customer insights, and improve operational inefficiencies.

In this blog post, we showcase a curated gallery of visuals generated by Nova Canvas—categorized by real-world use cases—from marketing and product visualization to concept art and design exploration. Each image is paired with the prompt and parameters that generated it, providing a practical starting point for your own AI-driven creativity. Whether you’re crafting specific types of images, optimizing workflows, or simply seeking inspiration, this guide will help you unlock the full potential of HAQM Nova Canvas.

A recent evaluation conducted by FloTorch compared the performance of HAQM Nova models with OpenAI’s GPT-4o. In this post, we discuss the findings from this benchmarking in more detail.

In this post, we walk through how to build the FoodSavr solution (fictitious name used for the purposes of this post) using HAQM Rekognition Custom Labels to detect the ingredients and generate personalized recipes using Anthropic’s Claude 3.0 on HAQM Bedrock. We demonstrate an end-to-end architecture where a user can upload an image of their fridge, and using the ingredients found there (detected by HAQM Rekognition), the solution will give them a list of recipes (generated by HAQM Bedrock). The architecture also recognizes missing ingredients and provides the user with a list of nearby grocery stores.

In this post, we demonstrate how Octus migrated its flagship product, CreditAI, to HAQM Bedrock, transforming how investment professionals access and analyze credit intelligence. We walk through the journey Octus took from managing multiple cloud providers and costly GPU instances to implementing a streamlined, cost-effective solution using AWS services including HAQM Bedrock, AWS Fargate, and HAQM OpenSearch Service.

In this post, we demonstrate how to optimize reasoning models like DeepSeek-R1 using prompt optimization on HAQM Bedrock.

Today, we’re announcing the general availability (GA) of multi-agent collaboration on HAQM Bedrock. This capability allows developers to build, deploy, and manage networks of AI agents that work together to execute complex, multi-step workflows efficiently.

This post is co-authored with Sundeep Sardana, Malolan Raman, Joseph Lam, Maitri Shah and Vaibhav Singh from Verisk. Verisk (Nasdaq: VRSK) is a leading strategic data analytics and technology partner to the global insurance industry, empowering clients to strengthen operating efficiency, improve underwriting and claims outcomes, combat fraud, and make informed decisions about global risks. […]