AI

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

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 integration and continuous delivery (CI/CD) practices,

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

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 with Strands agents. While these custom

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