Accelerate Generative AI Inference on Amazon SageMaker AI with G7e Instances | Amazon Web Services
As the demand for generative AI continues to grow, developers and enterprises seek more flexible, cost-effective, and powerful accelerators to…
Tag: SageMaker
Use-case based deployments on SageMaker JumpStart | Amazon Web Services
Amazon SageMaker JumpStart provides pretrained models for a wide range of problem types to help you get started with AI…
Unlock efficient model deployment: Simplified Inference Operator setup on Amazon SageMaker HyperPod | Amazon Web Services
Amazon SageMaker HyperPod offers an end-to-end experience supporting the full lifecycle of AI development—from interactive experimentation and training to inference…
Accelerate agentic tool calling with serverless model customization in Amazon SageMaker AI | Amazon Web Services
Agentic tool calling is what makes AI agents useful in production. It’s how they query databases, trigger workflows, retrieve real-time…
How to set up an air-gapped VPC for Amazon SageMaker Unified Studio | Amazon Web Services
Organizations are finding significant value using an integrated experience for all your data and AI with Amazon SageMaker Unified Studio. However, many…
Scaling seismic foundation models on AWS: Distributed training with Amazon SageMaker HyperPod and expanding context windows | Amazon Web Services
This post is cowritten with Altay Sansal and Alejandro Valenciano from TGS. TGS, a geoscience data provider for the energy…
Enhanced metrics for Amazon SageMaker AI endpoints: deeper visibility for better performance | Amazon Web Services
Running machine learning (ML) models in production requires more than just infrastructure resilience and scaling efficiency. You need nearly continuous…
Filter catalog assets using custom metadata search filters in Amazon SageMaker Unified Studio | Amazon Web Services
Finding the right data assets in large enterprise catalogs can be challenging, especially when thousands of datasets are cataloged with…
Building custom model provider for Strands Agents with LLMs hosted on SageMaker AI endpoints | Amazon Web Services
Organizations increasingly deploy custom large language models (LLMs) on Amazon SageMaker AI real-time endpoints using their preferred serving frameworks—such as…
Efficiently serve dozens of fine-tuned models with vLLM on Amazon SageMaker AI and Amazon Bedrock | Amazon Web Services
Organizations and individuals running multiple custom AI models, especially recent Mixture of Experts (MoE) model families, can face the challenge…