Accelerate custom LLM deployment: Fine-tune with Oumi and deploy to Amazon Bedrock

Favorite This post is cowritten by David Stewart and Matthew Persons from Oumi. Fine-tuning open source large language models (LLMs) often stalls between experimentation and production. Training configurations, artifact management, and scalable deployment each require different tools, creating friction when moving from rapid experimentation to secure, enterprise-grade environments. In this

Read More
Shared by AWS Machine Learning March 11, 2026

Access Anthropic Claude models in India on Amazon Bedrock with Global cross-Region inference

Favorite The adoption and implementation of generative AI inference has increased with organizations building more operational workloads that use AI capabilities in production at scale. To help customers achieve the scale of their generative AI applications, Amazon Bedrock offers cross-Region inference (CRIS) profiles. CRIS is a powerful feature that organizations

Read More
Shared by AWS Machine Learning March 10, 2026

OSI Adopts SPDX IDs for License URLs

Favorite For more than 25 years, the Open Source Initiative (OSI) website has been a central reference point for Open Source licenses. Over that time, our site has evolved through multiple redesigns, content management systems, and infrastructure migrations. As a result, license pages accumulated a variety of URL formats, including

Read More
Shared by voicesofopensource March 10, 2026

Building custom model provider for Strands Agents with LLMs hosted on SageMaker AI endpoints

Favorite Organizations increasingly deploy custom large language models (LLMs) on Amazon SageMaker AI real-time endpoints using their preferred serving frameworks—such as SGLang, vLLM, or TorchServe—to help gain greater control over their deployments, optimize costs, and align with compliance requirements. However, this flexibility introduces a critical technical challenge: response format incompatibility

Read More
Shared by AWS Machine Learning March 6, 2026

Drive organizational growth with Amazon Lex multi-developer CI/CD pipeline

Favorite As your conversational AI initiatives evolve, developing Amazon Lex assistants becomes increasingly complex. Multiple developers working on the same shared Lex instance leads to configuration conflicts, overwritten changes, and slower iteration cycles. Scaling Amazon Lex development requires isolated environments, version control, and automated deployment pipelines. By adopting well-structured continuous

Read More
Shared by AWS Machine Learning March 6, 2026