Category: Artificial Intelligence

In this post, we take an animated short film, Picchu, produced by FuzzyPixel from Amazon Web Services (AWS), prepare training data by extracting key character frames, and fine-tune a character-consistent model for the main character Mayu and her mother, so we can quickly generate storyboard concepts for new sequels like the following images.

The art of storyboarding stands as the cornerstone of modern content creation, weaving its essential role through filmmaking, animation, advertising, and UX design. Though traditionally, creators have relied on hand-drawn sequential illustrations to map their narratives, today’s AI foundation models (FMs) are transforming this landscape. FMs like Amazon Nova Canvas and Amazon Nova Reel offer […]

Proofpoint has redefined its professional services by integrating Amazon Q Business, a fully managed, generative AI powered assistant that you can configure to answer questions, provide summaries, generate content, and complete tasks based on your enterprise data. In this post, we explore how Amazon Q Business transformed Proofpoint’s professional services, detailing its deployment, functionality, and future roadmap.

In this post, we show how to deploy Coveo’s Passage Retrieval API as an Amazon Bedrock Agents action group to enhance response accuracy, so Coveo users can use their current index to rapidly deploy new generative experiences across their organization.

In this post, we demonstrate how to use the new Amazon SageMaker HyperPod CLI and SDK to streamline the process of training and deploying large AI models through practical examples of distributed training using Fully Sharded Data Parallel (FSDP) and model deployment for inference. The tools provide simplified workflows through straightforward commands for common tasks, while offering flexible development options through the SDK for more complex requirements, along with comprehensive observability features and production-ready deployment capabilities.

In this post, we introduce a flexible and scalable solution that simplifies the batch inference workflow. This solution provides a highly scalable approach to managing your FM batch inference needs, such as generating embeddings for millions of documents or running custom evaluation or completion tasks with large datasets.

In this post, we explore how natural language database analytics can revolutionize the way organizations interact with their structured data through the power of large language model (LLM) agents. Natural language interfaces to databases have long been a goal in data management. Agents enhance database analytics by breaking down complex queries into explicit, verifiable reasoning steps and enabling self-correction through validation loops that can catch errors, analyze failures, and refine queries until they accurately match user intent and schema requirements.

In this post, we demonstrated how to automate the deployment of Amazon Knowledge Bases for RAG applications using Terraform.

In this blog post, we demonstrate an end-to-end approach for building and evaluating a KIE solution using Amazon Nova models available through Amazon Bedrock. This end-to-end approach encompasses three critical phases: data readiness (understanding and preparing your documents), solution development (implementing extraction logic with appropriate models), and performance measurement (evaluating accuracy, efficiency, and cost-effectiveness). We illustrate this comprehensive approach using the FATURA dataset—a collection of diverse invoice documents that serves as a representative proxy for real-world enterprise data.

With the new cluster creation experience, you can create your SageMaker HyperPod clusters, including the required prerequisite AWS resources, in one click, with prescriptive default values automatically applied. In this post, we explore the new cluster creation experience for Amazon SageMaker HyperPod.