Category: Amazon SageMaker AI

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.

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.

In this post, we show how to process multi-page documents with a human review loop using Amazon Bedrock Data Automation and Amazon SageMaker AI.

AWS Batch now seamlessly integrates with Amazon SageMaker Training jobs. In this post, we discuss the benefits of managing and prioritizing ML training jobs to use hardware efficiently for your business. We also walk you through how to get started using this new capability and share suggested best practices, including the use of SageMaker training plans.

At the AWS Summit in New York City, we introduced a comprehensive suite of model customization capabilities for Amazon Nova foundation models. Available as ready-to-use recipes on Amazon SageMaker AI, you can use them to adapt Nova Micro, Nova Lite, and Nova Pro across the model training lifecycle, including pre-training, supervised fine-tuning, and alignment. In this post, we present a streamlined approach to customize Nova Micro in SageMaker training jobs.

Organizations are adopting large language models (LLMs), such as DeepSeek R1, to transform business processes, enhance customer experiences, and drive innovation at unprecedented speed. However, standalone LLMs have key limitations such as hallucinations, outdated knowledge, and no access to proprietary data. Retrieval Augmented Generation (RAG) addresses these gaps by combining semantic search with generative AI, […]

In this post, we share how Rapid7 implemented end-to-end automation for the training, validation, and deployment of ML models that predict CVSS vectors. Rapid7 customers have the information they need to accurately understand their risk and prioritize remediation measures.