Category: Amazon SageMaker

This post walks through how Harmonic Security used Amazon SageMaker AI, Amazon Bedrock, and Amazon Nova Pro to fine-tune a ModernBERT model, achieving low-latency, accurate, and scalable data leakage detection.

Today we’re announcing Amazon SageMaker AI with MLflow, now including a serverless capability that dynamically manages infrastructure provisioning, scaling, and operations for artificial intelligence and machine learning (AI/ML) development tasks. In this post, we explore how these new capabilities help you run large MLflow workloads—from generative AI agents to large language model (LLM) experimentation—with improved performance, automation, and security using SageMaker AI with MLflow.

In this post, we introduce Managed Tiered KV Cache and Intelligent Routing for Amazon SageMaker HyperPod, new capabilities that can reduce time to first token by up to 40% and lower compute costs by up to 25% for long context prompts and multi-turn conversations. These features automatically manage distributed KV caching infrastructure and intelligent request routing, making it easier to deploy production-scale LLM inference workloads with enterprise-grade performance while significantly reducing operational overhead.

In this post, we explore how Condé Nast used Amazon Bedrock and Anthropic’s Claude to accelerate their contract processing and rights analysis workstreams. The company’s extensive portfolio, spanning multiple brands and geographies, required managing an increasingly complex web of contracts, rights, and licensing agreements.

In this post, we will walk through the performance constraints and design choices by OARC and REMAP teams at UCLA, including how AWS serverless infrastructure, AWS Managed Services, and generative AI services supported the rapid design and deployment of our solution. We will also describe our use of Amazon SageMaker AI and how it can be used reliably in immersive live experiences.

This blog post introduces the new Amazon Nova model evaluation features in Amazon SageMaker AI. This release adds custom metrics support, LLM-based preference testing, log probability capture, metadata analysis, and multi-node scaling for large evaluations.

Amazon SageMaker AI now supports EAGLE-based adaptive speculative decoding, a technique that accelerates large language model inference by up to 2.5x while maintaining output quality. In this post, we explain how to use EAGLE 2 and EAGLE 3 speculative decoding in Amazon SageMaker AI, covering the solution architecture, optimization workflows using your own datasets or SageMaker’s built-in data, and benchmark results demonstrating significant improvements in throughput and latency.

In this post, we demonstrate how to migrate computer vision workloads from Amazon Lookout for Vision to Amazon SageMaker AI by training custom defect detection models using pre-trained models available on AWS Marketplace. We provide step-by-step guidance on labeling datasets with SageMaker Ground Truth, training models with flexible hyperparameter configurations, and deploying them for real-time or batch inference—giving you greater control and flexibility for automated quality inspection use cases.

We’re introducing bidirectional streaming for Amazon SageMaker AI Inference, which transforms inference from a transactional exchange into a continuous conversation. This post shows you how to build and deploy a container with bidirectional streaming capability to a SageMaker AI endpoint. We also demonstrate how you can bring your own container or use our partner Deepgram’s pre-built models and containers on SageMaker AI to enable bi-directional streaming feature for real-time inference.

In this post, we explore how Amazon SageMaker HyperPod now supports NVIDIA Multi-Instance GPU (MIG) technology, enabling you to partition powerful GPUs into multiple isolated instances for running concurrent workloads like inference, research, and interactive development. By maximizing GPU utilization and reducing wasted resources, MIG helps organizations optimize costs while maintaining performance isolation and predictable quality of service across diverse machine learning tasks.