Artificial Intelligence

Category: Amazon SageMaker AI

Document intelligence evolved: Building and evaluating KIE solutions that scale

In this blog post, we demonstrate an end-to-end approach for building and evaluating a KIE solution using Amazon Nova models available through Amazon Bedrock. This end-to-end approach encompasses three critical phases: data readiness (understanding and preparing your documents), solution development (implementing extraction logic with appropriate models), and performance measurement (evaluating accuracy, efficiency, and cost-effectiveness). We illustrate this comprehensive approach using the FATURA dataset—a collection of diverse invoice documents that serves as a representative proxy for real-world enterprise data.

Amazon SageMaker HyperPod enhances ML infrastructure with scalability and customizability

In this post, we introduced three features in SageMaker HyperPod that enhance scalability and customizability for ML infrastructure. Continuous provisioning offers flexible resource provisioning to help you start training and deploying your models faster and manage your cluster more efficiently. With custom AMIs, you can align your ML environments with organizational security standards and software requirements.

Fine-tune OpenAI GPT-OSS models using Amazon SageMaker HyperPod recipes

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, […]

Enhance AI agents using predictive ML models with Amazon SageMaker AI and Model Context Protocol (MCP)

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.

architecture diagram showing trusted identity propagation between multiple aws services

Simplify access control and auditing for Amazon SageMaker Studio using trusted identity propagation

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.

Optimizing Salesforce’s model endpoints with Amazon SageMaker AI inference components

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.

bda validation

Scalable intelligent document processing using Amazon Bedrock Data Automation

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.

How Indegene’s AI-powered social intelligence for life sciences turns social media conversations into insights

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.

Automate AIOps with Amazon SageMaker Unified Studio projects, Part 1: Solution architecture

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.

Fine-tune OpenAI GPT-OSS models on Amazon SageMaker AI using Hugging Face libraries

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.