Category: Amazon SageMaker

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.

In this two-part series, we discuss how you can reduce the DeepSeek model customization complexity by using the pre-built fine-tuning workflows (also called “recipes”) for both DeepSeek-R1 model and its distilled variations, released as part of Amazon SageMaker HyperPod recipes. In this first post, we will build a solution architecture for fine-tuning DeepSeek-R1 distilled models and demonstrate the approach by providing a step-by-step example on customizing the DeepSeek-R1 Distill Qwen 7b model using recipes, achieving an average of 25% on all the Rouge scores, with a maximum of 49% on Rouge 2 score with both SageMaker HyperPod and SageMaker training jobs. The second part of the series will focus on fine-tuning the DeepSeek-R1 671b model itself.

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.

In this post, we share how we modernized Rocket Companies’ data science solution on AWS to increase the speed to delivery from eight weeks to under one hour, improve operational stability and support by reducing incident tickets by over 99% in 18 months, power 10 million automated data science and AI decisions made daily, and provide a seamless data science development experience.

In this post, we present the continuous self-instruct fine-tuning framework as a compound AI system implemented by the DSPy framework. The framework first generates a synthetic dataset from the domain knowledge base and documents for self-instruction, then drives model fine-tuning through SFT, and introduces the human-in-the-loop workflow to collect human and AI feedback to the model response, which is used to further improve the model performance by aligning human preference through reinforcement learning (RLHF/RLAIF).

In this post, we provide best practices to maximize the value of SageMaker HyperPod task governance and make the administration and data science experiences seamless. We also discuss common governance scenarios when administering and running generative AI development tasks.

Researchers developed Medusa, a framework to speed up LLM inference by adding extra heads to predict multiple tokens simultaneously. This post demonstrates how to use Medusa-1, the first version of the framework, to speed up an LLM by fine-tuning it on Amazon SageMaker AI and confirms the speed up with deployment and a simple load test. Medusa-1 achieves an inference speedup of around two times without sacrificing model quality, with the exact improvement varying based on model size and data used. In this post, we demonstrate its effectiveness with a 1.8 times speedup observed on a sample dataset.

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.

GraphStorm is a low-code enterprise graph machine learning (ML) framework that provides ML practitioners a simple way of building, training, and deploying graph ML solutions on industry-scale graph data. In this post, we demonstrate how GraphBolt enhances GraphStorm’s performance in distributed settings. We provide a hands-on example of using GraphStorm with GraphBolt on SageMaker for distributed training. Lastly, we share how to use Amazon SageMaker Pipelines with GraphStorm.