Category: Amazon SageMaker

Learn how Articul8 is redefining enterprise generative AI with domain-specific models that outperform general-purpose LLMs in real-world applications. In our latest blog post, we dive into how Amazon SageMaker HyperPod accelerated the development of Articul8’s industry-leading semiconductor model—achieving 2X higher accuracy that top open source models while slashing deployment time by 4X.

The Llama 3.3 Nemotron Super 49B V1 and Llama 3.1 Nemotron Nano 8B V1 are now available in Amazon Bedrock Marketplace and Amazon SageMaker JumpStart. With this launch, you can now deploy NVIDIA’s newest reasoning models to build, experiment, and responsibly scale your generative AI ideas on AWS.

In this post, we demonstrate how to use large vision models (LVMs) for semantic video search using natural language and image queries. We introduce some use case-specific methods, such as temporal frame smoothing and clustering, to enhance the video search performance. Furthermore, we demonstrate the end-to-end functionality of this approach by using both asynchronous and real-time hosting options on Amazon SageMaker AI to perform video, image, and text processing using publicly available LVMs on the Hugging Face Model Hub. Finally, we use Amazon OpenSearch Serverless with its vector engine for low-latency semantic video search.

In this post, we discuss how an enterprise with multiple accounts can access a shared Amazon SageMaker HyperPod cluster for running their heterogenous workloads. We use SageMaker HyperPod task governance to enable this feature.

Radial is the largest 3PL fulfillment provider, also offering integrated payment, fraud detection, and omnichannel solutions to mid-market and enterprise brands. In this post, we share how Radial optimized the cost and performance of their fraud detection machine learning (ML) applications by modernizing their ML workflow using Amazon SageMaker.

In this post, we demonstrate how to deploy a small language model on SageMaker AI by extending our pre-built containers to be compatible with AWS Graviton instances. We first provide an overview of the solution, and then provide detailed implementation steps to help you get started. You can find the example notebook in the GitHub repo.

In this post, we share how Impel enhances the automotive dealership customer experience with fine-tuned LLMs on SageMaker.

In this post you define, deploy, and provision a SageMaker Project custom template purely in Terraform. With no dependencies on other IaC tools, you can now enable SageMaker Projects strictly within your Terraform Enterprise infrastructure.

ZURU collaborated with AWS Generative AI Innovation Center and AWS Professional Services to implement a more accurate text-to-floor plan generator using generative AI. In this post, we show you why a solution using a large language model (LLM) was chosen. We explore how model selection, prompt engineering, and fine-tuning can be used to improve results.

In this post, we explore how a leading GeoFM (Clay Foundation’s Clay foundation model available on Hugging Face) can be deployed for large-scale inference and fine-tuning on Amazon SageMaker.