Artificial Intelligence
Author: Melanie Li
Melanie, PhD, is a Senior Generative AI Specialist Solutions Architect at AWS based in Sydney, Australia, where her focus is on working with customers to build solutions using state-of-the-art AI/ML tools. She has been actively involved in multiple generative AI initiatives across APJ, harnessing the power of LLMs. Prior to joining AWS, Dr. Li held data science roles in the financial and retail industries.
Introducing cross-Region inference for OpenAI GPT-5.6 models on Amazon Bedrock
Amazon Bedrock now offers OpenAI GPT-5.6 models (Sol, Terra, and Luna) in more than 25 AWS Regions with cross-Region inference. Learn how US geographic and global inference profiles route requests for higher throughput, how to call the models with the OpenAI and Converse APIs, and how to configure IAM, quotas, and monitoring.
Introducing explicit prompt caching for OpenAI GPT-5.6 models on Amazon Bedrock
OpenAI GPT-5.6 Sol, Terra, and Luna are now generally available on Amazon Bedrock, along with explicit prompt caching that gives you precise control over which parts of your prompt are cached and reused. Learn how to get started, set up explicit caching, and migrate existing GPT workloads to reduce inference cost.
Introducing Grok on Amazon Bedrock
This post covers what makes Grok 4.3 a great fit for agentic and enterprise workloads, how you access it through Amazon Bedrock, and how to use the capabilities most teams reach for first: a basic chat request, configurable reasoning effort, tool calling, structured output, image input, and stateful multi-turn conversations.
Unlock global AI inference scalability using new global cross-Region inference on Amazon Bedrock with Anthropic’s Claude Sonnet 4.5
This post is co-written with Gareth Jones from Anthropic. Organizations are increasingly integrating generative AI capabilities into their applications to enhance customer experiences, streamline operations, and drive innovation. As generative AI workloads continue to grow in scale and importance, organizations face new challenges in maintaining consistent performance, reliability, and availability of their AI-powered applications. Customers […]
Migrate from Anthropic’s Claude Sonnet 3.x to Claude Sonnet 4.x on Amazon Bedrock
This post provides a systematic approach to migrating from Anthropic’s Claude 3.5 Sonnet to Claude Sonnet 4.5 on Amazon Bedrock. We examine the key model differences, highlight essential migration considerations, and deliver proven best practices to transform this necessary transition into a strategic advantage that drives measurable value for your organization.
Build and deploy AI inference workflows with new enhancements to the Amazon SageMaker Python SDK
In this post, we provide an overview of the user experience, detailing how to set up and deploy these workflows with multiple models using the SageMaker Python SDK. We walk through examples of building complex inference workflows, deploying them to SageMaker endpoints, and invoking them for real-time inference.
Enhance deployment guardrails with inference component rolling updates for Amazon SageMaker AI inference
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.
Introducing Fast Model Loader in SageMaker Inference: Accelerate autoscaling for your Large Language Models (LLMs) – Part 2
In this post, we provide a detailed, hands-on guide to implementing Fast Model Loader in your LLM deployments. We explore two approaches: using the SageMaker Python SDK for programmatic implementation, and using the Amazon SageMaker Studio UI for a more visual, interactive experience. Whether you’re a developer who prefers working with code or someone who favors a graphical interface, you’ll learn how to take advantage of this powerful feature to accelerate your LLM deployments.
Package and deploy classical ML and LLMs easily with Amazon SageMaker, part 1: PySDK Improvements
Amazon SageMaker is a fully managed service that enables developers and data scientists to quickly and effortlessly build, train, and deploy machine learning (ML) models at any scale. SageMaker makes it straightforward to deploy models into production directly through API calls to the service. Models are packaged into containers for robust and scalable deployments. Although […]
Efficiently train, tune, and deploy custom ensembles using Amazon SageMaker
Artificial intelligence (AI) has become an important and popular topic in the technology community. As AI has evolved, we have seen different types of machine learning (ML) models emerge. One approach, known as ensemble modeling, has been rapidly gaining traction among data scientists and practitioners. In this post, we discuss what ensemble models are and […]