Category: Foundation models

In this post, we guide you through the stages of customizing large language models (LLMs) with SageMaker Unified Studio and SageMaker AI, covering the end-to-end process starting from data discovery to fine-tuning FMs with SageMaker AI distributed training, tracking metrics using MLflow, and then deploying models using SageMaker AI inference for real-time inference. We also discuss best practices to choose the right instance size and share some debugging best practices while working with JupyterLab notebooks in SageMaker Unified Studio.

In this post, we shared how an internal technology team at Amazon evaluated Amazon Nova models, resulting in notable improvements in inference speed and cost-efficiency.

You can now import custom weights for Qwen2, Qwen2_VL, and Qwen2_5_VL architectures, including models like Qwen 2, 2.5 Coder, Qwen 2.5 VL, and QwQ 32B. In this post, we cover how to deploy Qwen 2.5 models with Amazon Bedrock Custom Model Import, making them accessible to organizations looking to use state-of-the-art AI capabilities within the AWS infrastructure at an effective cost.

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.

In this post, we illustrate how VideoAmp, a media measurement company, worked with the AWS Generative AI Innovation Center (GenAIIC) team to develop a prototype of the VideoAmp Natural Language (NL) Analytics Chatbot to uncover meaningful insights at scale within media analytics data using Amazon Bedrock.

Contextual retrieval enhances traditional RAG by adding chunk-specific explanatory context to each chunk before generating embeddings. This approach enriches the vector representation with relevant contextual information, enabling more accurate retrieval of semantically related content when responding to user queries. In this post, we demonstrate how to use contextual retrieval with Anthropic and Amazon Bedrock Knowledge Bases.

In this post, we’ll explore how to combine Amazon Bedrock prompt caching with Claude Code—a coding agent released by Anthropic that is now generally available. This powerful combination transforms your development workflow by delivering lightning-fast responses from reducing inference response latency, as well as lowering input token costs.

We’ve witnessed remarkable advances in model capabilities as generative AI companies have invested in developing their offerings. Language models such as Anthropic’s Claude Opus 4 & Sonnet 4 and Amazon Nova on Amazon Bedrock can reason, write, and generate responses with increasing sophistication. But even as these models grow more powerful, they can only work […]

When a regulatory body like the US Food and Drug Administration (FDA) introduces changes to regulations, organizations are required to evaluate the changes against their internal SOPs. When necessary, they must update their SOPs to align with the regulation changes and maintain compliance. In this post, we show different approaches using Amazon Bedrock to identify relationships between regulation changes and SOPs.

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.