Category: Amazon SageMaker

In this post, we discuss the challenges faced by organizations when updating models in production. Then we deep dive into the new rolling update feature for inference components and provide practical examples using DeepSeek distilled models to demonstrate this feature. Finally, we explore how to set up rolling updates in different scenarios.

Today, we’re excited to announce the general availability of Amazon Bedrock Data Automation, a powerful, fully managed capability within Amazon Bedrock that seamlessly transforms unstructured multimodal data into structured, application-ready insights with high accuracy, cost efficiency, and scalability.

In this blog post, we explore how to integrate NeMo 2.0 with SageMaker HyperPod to enable efficient training of large language models (LLMs). We cover the setup process and provide a step-by-step guide to running a NeMo job on a SageMaker HyperPod cluster.

Today, we are excited to announce that the NeMo Retriever Llama3.2 Text Embedding and Reranking NVIDIA NIM microservices are available in Amazon SageMaker JumpStart. With this launch, you can now deploy NVIDIA’s optimized reranking and embedding models to build, experiment, and responsibly scale your generative AI ideas on AWS. In this post, we demonstrate how to get started with these models on SageMaker JumpStart.

In this post, we show how SageMaker HyperPod, and its new features introduced at AWS re:Invent 2024, is designed to meet the demands of modern AI workloads, offering a persistent and optimized cluster tailored for distributed training and accelerated inference at cloud scale and attractive price-performance.

In this post, we outline how to get started with deploying the Qwen 2.5 family of models on an Inferentia instance using Amazon Elastic Compute Cloud (Amazon EC2) and Amazon SageMaker using the Hugging Face Text Generation Inference (TGI) container and the Hugging Face Optimum Neuron library. Qwen2.5 Coder and Math variants are also supported.

In this post, we demonstrate how to optimize hosting DeepSeek-R1 distilled models with Hugging Face Text Generation Inference (TGI) on Amazon SageMaker AI.

Deploying DeepSeek models on SageMaker AI provides a robust solution for organizations seeking to use state-of-the-art language models in their applications. In this post, we show how to use the distilled models in SageMaker AI, which offers several options to deploy the distilled versions of the R1 model.

In this blog post, we will guide you through the process of integrating Chronos into Amazon SageMaker Pipeline using a synthetic dataset that simulates a sales forecasting scenario, unlocking accurate and efficient predictions with minimal data.

In this post, we discuss best practices for applying LLMs to generate ground truth for evaluating question-answering assistants with FMEval on an enterprise scale. FMEval is a comprehensive evaluation suite from Amazon SageMaker Clarify, and provides standardized implementations of metrics to assess quality and responsibility. To learn more about FMEval, see Evaluate large language models for quality and responsibility of LLMs.