Category: Amazon SageMaker

In this post, we share how Qualtrics built an AI platform powered by Amazon SageMaker and Amazon Bedrock.

In this post, we use the recipes to fine-tune the original DeepSeek-R1 671b parameter model. We demonstrate this through the step-by-step implementation of these recipes using both SageMaker training jobs and SageMaker HyperPod.

In this post, we explore how AWS services can be seamlessly integrated with open source tools to help establish a robust red teaming mechanism within your organization. Specifically, we discuss Data Reply’s red teaming solution, a comprehensive blueprint to enhance AI safety and responsible AI practices.

This post demonstrates how AWS LLM League’s gamified enablement accelerates partners’ practical AI development capabilities, while showcasing how fine-tuning smaller language models can deliver cost-effective, specialized solutions for specific industry needs.

In this post, we discuss how you can build an AI-powered document processing platform with open source NER and LLMs on SageMaker.

Today, we’re excited to announce the launch of Amazon SageMaker Large Model Inference (LMI) container v15, powered by vLLM 0.8.4 with support for the vLLM V1 engine. This release introduces significant performance improvements, expanded model compatibility with multimodality (that is, the ability to understand and analyze text-to-text, images-to-text, and text-to-images data), and provides built-in integration with vLLM to help you seamlessly deploy and serve large language models (LLMs) with the highest performance at scale.

This post is a joint collaboration between Salesforce and AWS and is being cross-published on both the Salesforce Engineering Blog and the AWS Machine Learning Blog. The Salesforce AI Model Serving team is working to push the boundaries of natural language processing and AI capabilities for enterprise applications. Their key focus areas include optimizing large […]

This post demonstrates how to deploy and serve the Mixtral 8x7B language model on AWS Inferentia2 instances for cost-effective, high-performance inference. We’ll walk through model compilation using Hugging Face Optimum Neuron, which provides a set of tools enabling straightforward model loading, training, and inference, and the Text Generation Inference (TGI) Container, which has the toolkit for deploying and serving LLMs with Hugging Face.

In this post, we explore the challenges of large-scale frontier model training, focusing on hardware failures and the benefits of Amazon SageMaker HyperPod – a solution that minimizes disruptions, enhances efficiency, and reduces training costs.

The introduction of Amazon Nova models represent a significant advancement in the field of AI, offering new opportunities for large language model (LLM) optimization. In this post, we demonstrate how to effectively perform model customization and RAG with Amazon Nova models as a baseline. We conducted a comprehensive comparison study between model customization and RAG using the latest Amazon Nova models, and share these valuable insights.