Category: Announcements

Today, Amazon Web Services (AWS) announced the general availability of Amazon Bedrock Knowledge Bases GraphRAG (GraphRAG), a capability in Amazon Bedrock Knowledge Bases that enhances Retrieval-Augmented Generation (RAG) with graph data in Amazon Neptune Analytics. In this post, we discuss the benefits of GraphRAG and how to get started with it in Amazon Bedrock Knowledge Bases.

We’re excited to announce that Mistral-Small-24B-Instruct-2501—a twenty-four billion parameter large language model (LLM) from Mistral AI that’s optimized for low latency text generation tasks—is available for customers through Amazon SageMaker JumpStart and Amazon Bedrock Marketplace. In this post, we walk through how to discover, deploy, and use Mistral-Small-24B-Instruct-2501.

We are excited to announce that the Falcon 3 family of models from TII are available in Amazon SageMaker JumpStart. In this post, we explore how to deploy this model efficiently on Amazon SageMaker AI.

DeepSeek-R1 is an advanced large language model that combines reinforcement learning, chain-of-thought reasoning, and a Mixture of Experts architecture to deliver efficient, interpretable responses while maintaining safety through Amazon Bedrock Guardrails integration.

In this post, we demonstrate how to deploy distilled versions of DeepSeek-R1 models using Amazon Bedrock Custom Model Import. We focus on importing the variants currently supported DeepSeek-R1-Distill-Llama-8B and DeepSeek-R1-Distill-Llama-70B, which offer an optimal balance between performance and resource efficiency.

Today, we are excited to announce that the Llama 3.3 70B from Meta is available in Amazon SageMaker JumpStart. Llama 3.3 70B marks an exciting advancement in large language model (LLM) development, offering comparable performance to larger Llama versions with fewer computational resources. In this post, we explore how to deploy this model efficiently on Amazon SageMaker AI, using advanced SageMaker AI features for optimal performance and cost management.

In Part 1 of this series, we introduced the newly launched ModelTrainer class on the Amazon SageMaker Python SDK and its benefits, and showed you how to fine-tune a Meta Llama 3.1 8B model on a custom dataset. In this post, we look at the enhancements to the ModelBuilder class, which lets you seamlessly deploy a model from ModelTrainer to a SageMaker endpoint, and provides a single interface for multiple deployment configurations.

In this post, we focus on the ModelTrainer class for simplifying the training experience. The ModelTrainer class provides significant improvements over the current Estimator class, which are discussed in detail in this post. We show you how to use the ModelTrainer class to train your ML models, which includes executing distributed training using a custom script or container. In Part 2, we show you how to build a model and deploy to a SageMaker endpoint using the improved ModelBuilder class.

In this post, we examine how these features enhance the capabilities of Amazon Q Apps. We explore the new customization options, detailing how these advancements make Amazon Q Apps more accessible and applicable to a wider range of enterprise customers. We focus on key features such as custom labels, verified apps, private sharing, and data collection apps (preview).

Today, we are excited to announce that Pixtral 12B (pixtral-12b-2409), a state-of-the-art vision language model (VLM) from Mistral AI that excels in both text-only and multimodal tasks, is available for customers through Amazon SageMaker JumpStart. You can try this model with SageMaker JumpStart, a machine learning (ML) hub that provides access to algorithms and models that can be deployed with one click for running inference. In this post, we walk through how to discover, deploy, and use the Pixtral 12B model for a variety of real-world vision use cases.