Author: Melanie Li

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 are looking to scale their AI inference workloads across […]

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.

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.

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.

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.

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 […]

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 […]

Sometimes it can be very beneficial to use tools such as compilers that can modify and compile your models for optimal inference performance. In this post, we explore TensorRT and how to use it with Amazon SageMaker inference using NVIDIA Triton Inference Server. We explore how TensorRT works and how to host and optimize these […]

Renewable resources like sunlight provide a sustainable and carbon neutral mechanism to generate power. Governments in many countries are providing incentives and subsidies to households to install solar panels as part of small-scale renewable energy schemes. This has created a huge demand for solar panels. Reaching out to potential customers at the right time, through […]