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

AWS Machine Learning

To move faster, enterprises need robust operating models and a holistic approach that simplifies the generative AI lifecycle. Model monitoring – The model monitoring service allows tenants to evaluate model performance against predefined metrics. Finally, you can build your own evaluation pipelines and use tools such as fmeval.

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Mastering customer health for complex enterprise relationships

Totango

For enterprise organizations, managing customer relationships is far from simple. For enterprises, a well-constructed customer health score isnt just a nice-to-have; its a strategic asset that empowers teams to manage complexity, sustain customer satisfaction, and scale their customer success efforts.

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Generative AI operating models in enterprise organizations with Amazon Bedrock

AWS Machine Learning

Large organizations often have many business units with multiple lines of business (LOBs), with a central governing entity, and typically use AWS Organizations with an Amazon Web Services (AWS) multi-account strategy. LOBs have autonomy over their AI workflows, models, and data within their respective AWS accounts.

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How 24/7 Call Centers Improve Your Customer Experience

TeleDirect

Advanced Analytics Monitor call center performance metrics, such as resolution times and customer satisfaction scores. Financial Services Provide account support and fraud detection. Q: What metrics are used to measure the success of a 24/7 call center? Enable personalized support by providing agents with relevant information.

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Benchmarking Amazon Nova and GPT-4o models with FloTorch

AWS Machine Learning

These models offer enterprises a range of capabilities, balancing accuracy, speed, and cost-efficiency. Using its enterprise software, FloTorch conducted an extensive comparison between Amazon Nova models and OpenAIs GPT-4o models with the Comprehensive Retrieval Augmented Generation (CRAG) benchmark dataset.

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Centralize model governance with SageMaker Model Registry Resource Access Manager sharing

AWS Machine Learning

We recently announced the general availability of cross-account sharing of Amazon SageMaker Model Registry using AWS Resource Access Manager (AWS RAM) , making it easier to securely share and discover machine learning (ML) models across your AWS accounts. Mitigation strategies : Implementing measures to minimize or eliminate risks.

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Evaluate RAG responses with Amazon Bedrock, LlamaIndex and RAGAS

AWS Machine Learning

This blog post delves into how these innovative tools synergize to elevate the performance of your AI applications, ensuring they not only meet but exceed the exacting standards of enterprise-level deployments. More sophisticated metrics are needed to evaluate factual alignment and accuracy.

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