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Generating value from enterprise data: Best practices for Text2SQL and generative AI

AWS Machine Learning

Monitoring – Logs and metrics around query parsing, prompt recognition, SQL generation, and SQL results should be collected to monitor the text-to-SQL LLM system. This is where providing vector embeddings of a centralized or unified data catalog to the LLMs results in more accurate and comprehensive information returned by the LLMs.

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Achieve operational excellence with well-architected generative AI solutions using Amazon Bedrock

AWS Machine Learning

Large enterprises are building strategies to harness the power of generative AI across their organizations. This integration makes sure enterprises can take advantage of the full power of generative AI while adhering to best practices in operational excellence. What’s different about operating generative AI workloads and solutions?

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Ground truth curation and metric interpretation best practices for evaluating generative AI question answering using FMEval

AWS Machine Learning

This post focuses on evaluating and interpreting metrics using FMEval for question answering in a generative AI application. FMEval is a comprehensive evaluation suite from Amazon SageMaker Clarify , providing standardized implementations of metrics to assess quality and responsibility. Question Answer Fact Who is Andrew R.

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The Evolving Chief Customer Officer: Identifying Value, Authority, Scope, Responsibilities, and Strategic Direction Within the Enterprise

Beyond Philosophy

Reflective of the escalating focus on customer data, experiences, and relationships across all methods of communication and access, the role is rapidly evolving and morphing; however, there is general agreement regarding its significance in building and sustaining true value, planning capability, and enterprise customer-centricity.

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Executive Report: The Customer Data Too Often Overlooked by the C-Suite

A recent Calabrio research study of more than 1,000 C-Suite executives has revealed leaders are missing a key data stream – voice of the customer data. Download the report to learn how executives can find and use VoC data to make more informed business decisions.

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Achieve enterprise-grade monitoring for your Amazon SageMaker models using Fiddler

AWS Machine Learning

Without enterprise-class model monitoring , your models may be decaying in silence. Fiddler , an enterprise-class Model Performance Management solution available on the AWS Marketplace , offers model monitoring and explainable AI to help ML teams inspect and address a comprehensive range of model issues. Don’t fret. Conclusion.

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MLOps foundation roadmap for enterprises with Amazon SageMaker

AWS Machine Learning

As enterprise businesses embrace machine learning (ML) across their organizations, manual workflows for building, training, and deploying ML models tend to become bottlenecks to innovation. Building an MLOps foundation that can cover the operations, people, and technology needs of enterprise customers is challenging. About the Author.