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Transforming financial analysis with CreditAI on Amazon Bedrock: Octus’s journey with AWS

AWS Machine Learning Blog

We walk through the journey Octus took from managing multiple cloud providers and costly GPU instances to implementing a streamlined, cost-effective solution using AWS services including Amazon Bedrock, AWS Fargate , and Amazon OpenSearch Service. Along the way, it also simplified operations as Octus is an AWS shop more generally.

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

AWS Machine Learning Blog

Generate accurate training data for SageMaker models – For model training, data scientists can use Tecton’s SDK within their SageMaker notebooks to retrieve historical features. The following graphic shows how Amazon Bedrock is incorporated to support generative AI capabilities in the fraud detection system architecture.

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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 a dynamic, role-based AI agent using Amazon Bedrock inline agents

AWS Machine Learning Blog

AWS Lambda functions for executing specific actions (such as submitting vacation requests or expense reports). A code interpreter tool for performing calculations and data analysis. To understand how this dynamic role-based functionality works under the hood, lets examine the following system architecture diagram.

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Data integration

Dataconomy

Data integration plays a key role in achieving this by incorporating data cleansing techniques, ensuring that the information used is accurate and consistent. Reduction of data silos Breaking down data silos is essential for enhancing collaboration across different departments within an organization.

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Multi-account support for Amazon SageMaker HyperPod task governance

AWS Machine Learning Blog

Organizations building or adopting generative AI use GPUs to run simulations, run inference (both for internal or external usage), build agentic workloads, and run data scientists’ experiments. The workloads range from ephemeral single-GPU experiments run by scientists to long multi-node continuous pre-training runs.

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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.