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Build a multi-tenant generative AI environment for your enterprise on AWS

AWS Machine Learning

In the first part of the series, we showed how AI administrators can build a generative AI software as a service (SaaS) gateway to provide access to foundation models (FMs) on Amazon Bedrock to different lines of business (LOBs). It also uses a number of other AWS services such as Amazon API Gateway , AWS Lambda , and Amazon SageMaker.

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Build a video insights and summarization engine using generative AI with Amazon Bedrock

AWS Machine Learning

To enable the video insights solution, the architecture uses a combination of AWS services, including the following: Amazon API Gateway is a fully managed service that makes it straightforward for developers to create, publish, maintain, monitor, and secure APIs at scale.

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Model customization, RAG, or both: A case study with Amazon Nova

AWS Machine Learning

On the Configure data source page, provide the following information: Specify the Amazon S3 location of the documents. Fine-tune an Amazon Nova model using the Amazon Bedrock API In this section, we provide detailed walkthroughs on fine-tuning and hosting customized Amazon Nova models using Amazon Bedrock. Specify a chunking strategy.

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Transforming credit decisions using generative AI with Rich Data Co and AWS

AWS Machine Learning

Its software-as-a-service (SaaS) solution empowers leading banks and lenders with deep customer insights and AI-driven decision-making capabilities. They provide access to external data and APIs or enable specific actions and computation. This post is co-written with Gordon Campbell, Charles Guan, and Hendra Suryanto from RDC.

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Enhance deployment guardrails with inference component rolling updates for Amazon SageMaker AI inference

AWS Machine Learning

For more information about the SageMaker AI API, refer to the SageMaker AI API Reference. 8B-Instruct to DeepSeek-R1-Distill-Llama-8B, but the new model version has different API expectations. In this use case, you have configured a CloudWatch alarm to monitor for 4xx errors, which would indicate API compatibility issues.

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Empower your business users to extract insights from company documents using Amazon SageMaker Canvas Generative AI

AWS Machine Learning

For example, business teams can now formulate responses consistent with an organization’s specific vocabulary and tenets, and can more quickly query lengthy documents to get responses specific and grounded to the contents of those documents. We describe the set-up process in detail in Setting up Canvas to query documents.

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Revolutionizing business processes with Amazon Bedrock and Appian’s generative AI skills

AWS Machine Learning

Current challenges faced by enterprises Modern enterprises face numerous challenges, including: Managing vast amounts of unstructured data: Enterprises deal with immense volumes of data generated from various sources such as emails, documents, and customer interactions.