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How KTern.AI built agentic AI for SAP on Amazon Bedrock AgentCore

Favorite In this post, learn how KTern.AI, an SAP digital transformation platform, used Amazon Bedrock AgentCore to build and deploy AI agents ready for enterprise-scale SAP transformation workloads. These agents autonomously orchestrate workflows from reverse engineering, fit-to-standard, and code analysis to exception mining in Finance and Sales processes. The result

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Shared by AWS Machine Learning July 11, 2026

Deploying quantized models on Amazon SageMaker AI with Unsloth

Favorite This post was co-written with Daniel Han and Michael Han from Unsloth. Deploying large foundation models (FMs) stored at their original 16-bit floating-point precision (BF16 or FP16) is expensive. They need large GPU instances, driving up serving costs, and slowing down iteration cycles. Quantization addresses this by reducing the

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Shared by AWS Machine Learning July 11, 2026

Fine-tune NVIDIA Nemotron 3 models with Amazon SageMaker AI serverless model customization

Favorite Model customization transforms general-purpose AI models into specialized enterprise assets. By fine-tuning foundation models (FMs) on domain-specific data, businesses teach AI their unique workflows, terminology, and deep domain specialization, along with strict adherence to brand voice and fewer hallucinations. For enterprises, this is more than an optimization. It’s the

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Shared by AWS Machine Learning July 11, 2026

Enhancing enterprise inference on Amazon SageMaker HyperPod with data capture, Hugging Face, NVMe, and Route 53 integration

Favorite As enterprises scale their generative AI workloads, the demand for faster, more observable, and more flexible inference infrastructure continues to grow. Amazon SageMaker HyperPod is rising to meet that challenge with a set of new capabilities designed to streamline how organizations deploy and operate large models in production. Teams

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Shared by AWS Machine Learning July 10, 2026