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Real value, real time: Production AI with Amazon SageMaker and Tecton

AWS Machine Learning Blog

Expand to generative AI use cases with your existing AWS and Tecton architecture After you’ve developed ML features using the Tecton and AWS architecture, you can extend your ML work to generative AI use cases. You can also find Tecton at AWS re:Invent. This process is shown in the following diagram.

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Innovating at speed: BMW’s generative AI solution for cloud incident analysis

AWS Machine Learning Blog

In this post, we explain how BMW uses generative AI technology on AWS to help run these digital services with high availability. Moreover, these teams might be geographically dispersed and run their workloads in different locations and regions; many hosted on AWS, some elsewhere.

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Build an AI-powered document processing platform with open source NER model and LLM on Amazon SageMaker

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Solution overview The NER & LLM Gen AI Application is a document processing solution built on AWS that combines NER and LLMs to automate document analysis at scale. The system then orchestrates the creation of necessary model endpoints, processes documents in batches for efficiency, and automatically cleans up resources upon completion.

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Going beyond AI assistants: Examples from Amazon.com reinventing industries with generative AI

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Throughout these examples, you will learn how the comprehensive suite of AWS services, including Amazon Bedrock and Amazon SageMaker , are the key to success. The team used AWS Fargate to orchestrate the workflow given its current integration into existing backend systems.

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Build multi-agent systems with LangGraph and Amazon Bedrock

AWS Machine Learning Blog

AWS has introduced a multi-agent collaboration capability for Amazon Bedrock Agents , enabling developers to build, deploy, and manage multiple AI agents working together on complex tasks. Stateful architecture Support for stateful and adaptive agents within a graph-based architecture enables more sophisticated behaviors and interactions.

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Rad AI reduces real-time inference latency by 50% using Amazon SageMaker

AWS Machine Learning Blog

Let’s transition to exploring solutions and architectural strategies. Approaches to researcher productivity To translate our strategic planning into action, we developed approaches focused on refining our processes and system architectures. He has a passion for continuous innovation and using data to drive business outcomes.

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Ray jobs on Amazon SageMaker HyperPod: scalable and resilient distributed AI

AWS Machine Learning Blog

Due to their massive size and the need to train on large amounts of data, FMs are often trained and deployed on large compute clusters composed of thousands of AI accelerators such as GPUs and AWS Trainium. Alternatively and recommended, you can deploy a ready-made EKS cluster with a single AWS CloudFormation template. The fsdp-ray.py