Category: Amazon SageMaker

In this post, we share best practices for reliable multi-turn RL training. We cover how to build a training environment you can trust, set up an external evaluation, design a reward aligned with the end task, manage what changes once the agent runs for multiple turns, and monitor the metrics that tell you when to iterate.

In this post, we demonstrate how to deploy BoltzGen on SageMaker AI and run an end-to-end protein design experiment. By the end of the walkthrough, you have a working setup that scales from quick validation runs to production batch processing. The setup offers two execution modes for different stages of research and uses step-level caching to reduce compute expenses during iterative workflows.

In this post, we explore how Outpost VFX achieved 8x faster training speeds using AWS infrastructure to transform their face replacement workflow, the technical architecture they implemented to overcome single-GPU limitations, and the measurable results achieved through AWS multi-GPU training.

In this post, you’ll learn how fine-tuning Amazon Nova models using Amazon SageMaker AI addresses these specific issues by teaching the models to recognize your exact data patterns, distinguish between similar fields, and process information more efficiently—achieving up to 94.77% extraction accuracy while reducing costs 50%.

This post shows you how to configure training jobs on Amazon SageMaker AI to get the most out of Blackwell’s architecture on AWS. You learn how to select batch sizes and sequence lengths that take advantage of Blackwell’s expanded memory, choose the right precision format for your model size (1B to 64B parameters), and apply activation checkpointing strategically. By the end, you have a practical framework for tuning your training configuration and launching distributed training jobs on P6-B200 instances.

In this post, we demonstrate how to implement video upscaling using SeedVR2 on SageMaker AI. We cover the solution architecture, walk through the deployment steps, and show performance comparisons that highlight the quality improvements and processing efficiency you can achieve. By the end of this post, you’ll have the practical knowledge needed to implement this super resolution solution.

In this post, we walk through how Huntington built a scalable AWS solution to detect and redact Personally Identifiable Information (PII) and Payment Card Industry (PCI) data from over 400 million documents, reducing processing time from years to just a few months while achieving 95%+ redaction accuracy.

In this post, we walk you through how to deploy ComfyUI workflows on Amazon SageMaker AI processing jobs to generate hundreds of high-quality images in a single batch. You learn how to set up the infrastructure using AWS Cloud Development Kit (AWS CDK), configure GPU-accelerated processing, and automate image generation at scale. You can then adapt this solution to your ComfyUI workflows specific to your needs. We will guide you through a practical, step-by-step process to automate ComfyUI workflows to generate hundreds of high-quality images in a single batch empowering you to scale your creative pipeline.

Amazon SageMaker AI provides fully managed real-time inference hosting for machine learning models. You deploy a model to a SageMaker endpoint backed by one or more compute instances, and SageMaker handles provisioning and scaling. SageMaker supports multiple endpoint architectures. This post focuses on the two most relevant to generative AI workloads with detailed observability: Single-model endpoints (SME) and Inference component (IC) endpoints.

Today, we’re announcing inline payload support for Amazon SageMaker AI Async Inference. Customers can now send inference payloads directly in the request body of the InvokeEndpointAsync API, removing the need to upload input data to Amazon Simple Storage Service (Amazon S3) before each invocation.