Artificial Intelligence
Category: Amazon SageMaker
Innovate business logic by implementing return of control in Amazon Bedrock Agents
In the context of distributed systems and microservices architecture, orchestrating communication between diverse components presents significant challenges. However, with the launch of Amazon Bedrock Agents, the landscape is evolving, offering a simplified approach to agent creation and seamless integration of the return of control capability. In this post, we explore how Amazon Bedrock Agents revolutionizes agent creation and demonstrates the efficacy of the return of control capability in orchestrating complex interactions between multiple systems.
Training Llama 3.3 Swallow: A Japanese sovereign LLM on Amazon SageMaker HyperPod
The Institute of Science Tokyo has successfully trained Llama 3.3 Swallow, a 70-billion-parameter large language model (LLM) with enhanced Japanese capabilities, using Amazon SageMaker HyperPod. The model demonstrates superior performance in Japanese language tasks, outperforming GPT-4o-mini and other leading models. This technical report details the training infrastructure, optimizations, and best practices developed during the project.
Accelerating Articul8’s domain-specific model development with Amazon SageMaker HyperPod
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.
NVIDIA Nemotron Super 49B and Nano 8B reasoning models now available in Amazon Bedrock Marketplace and Amazon SageMaker JumpStart
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.
Implement semantic video search using open source large vision models on Amazon SageMaker and Amazon OpenSearch Serverless
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.
Multi-account support for Amazon SageMaker HyperPod task governance
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.
Modernize and migrate on-premises fraud detection machine learning workflows to Amazon SageMaker
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.
Run small language models cost-efficiently with AWS Graviton and Amazon SageMaker AI
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.
Impel enhances automotive dealership customer experience with fine-tuned LLMs on Amazon SageMaker
In this post, we share how Impel enhances the automotive dealership customer experience with fine-tuned LLMs on SageMaker.
Deploy Amazon SageMaker Projects with Terraform Cloud
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.