Category: Amazon SageMaker

This post is the second part of the GPT-OSS series focusing on model customization with Amazon SageMaker AI. In Part 1, we demonstrated fine-tuning GPT-OSS models using open source Hugging Face libraries with SageMaker training jobs, which supports distributed multi-GPU and multi-node configurations, so you can spin up high-performance clusters on demand. In this post, […]

In this post, we walk through how you can use the new Code Editor and multiple spaces support in SageMaker Unified Studio. The sample solution shows how to develop an ML pipeline that automates the typical end-to-end ML activities to build, train, evaluate, and (optionally) deploy an ML model.

In this post, we demonstrate how to enhance AI agents’ capabilities by integrating predictive ML models using Amazon SageMaker AI and the MCP. By using the open source Strands Agents SDK and the flexible deployment options of SageMaker AI, developers can create sophisticated AI applications that combine conversational AI with powerful predictive analytics capabilities.

In this post, we explore how to enable and use trusted identity propagation in Amazon SageMaker Studio, which allows organizations to simplify access management by granting permissions to existing AWS IAM Identity Center identities. The solution demonstrates how to implement fine-grained access controls based on a physical user’s identity, maintain detailed audit logs across supported AWS services, and support long-running user background sessions for training jobs.

In this post, we share how the Salesforce AI Platform team optimized GPU utilization, improved resource efficiency and achieved cost savings using Amazon SageMaker AI, specifically inference components.

In the blog post Scalable intelligent document processing using Amazon Bedrock, we demonstrated how to build a scalable IDP pipeline using Anthropic foundation models on Amazon Bedrock. Although that approach delivered robust performance, the introduction of Amazon Bedrock Data Automation brings a new level of efficiency and flexibility to IDP solutions. This post explores how Amazon Bedrock Data Automation enhances document processing capabilities and streamlines the automation journey.

In this post, we review the technical specifications of P6e-GB200 UltraServers, discuss their performance benefits, and highlight key use cases. We then walk though how to purchase UltraServer capacity through flexible training plans and get started using UltraServers with SageMaker HyperPod.

This post explores how Indegene’s Social Intelligence Solution uses advanced AI to help life sciences companies extract valuable insights from digital healthcare conversations. Built on AWS technology, the solution addresses the growing preference of HCPs for digital channels while overcoming the challenges of analyzing complex medical discussions on a scale.

This post presents architectural strategies and a scalable framework that helps organizations manage multi-tenant environments, automate consistently, and embed governance controls as they scale their AI initiatives with SageMaker Unified Studio.

Released on August 5, 2025, OpenAI’s GPT-OSS models, gpt-oss-20b and gpt-oss-120b, are now available on AWS through Amazon SageMaker AI and Amazon Bedrock. In this post, we walk through the process of fine-tuning a GPT-OSS model in a fully managed training environment using SageMaker AI training jobs.