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Coles AI-Ready Data at Village Green Shopping Centre in Vernon, BC
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Build scalable AI systems by fixing data silos, quality decay, and governance gaps AI-Ready Data provides a structured roadmap for organizations deploying traditional AI, large language models, and agentic AI systems. Written by Andrew Madson, who has held data strategy leadership roles at Fortune 100 companies including JPMorganChase and MassMutual, the book focuses on the foundational data challenges that undermine AI outcomes. It connects AI engineering, data strategy, and infrastructure planning into a unified approach for building production-grade AI systems. The book details how to identify and resolve data silos, quality decay, and governance gaps that create hidden costs and erode AI ROI. It covers modern data product architectures and compliance-ready systems designed to accelerate model deployment and reduce technical debt. Each chapter addresses specific operational pain points, from dirty data remediation to building scalable infrastructure that supports traditional ML pipelines, LLM integration, and agentic AI workflows. Readers will also find: Strategies for diagnosing and eliminating data quality decay across enterprise data pipelines before it undermines AI model performance Frameworks for building modern data products and architectures that reduce technical debt and accelerate model deployment cycles Governance models designed to close compliance gaps and create audit-ready systems for AI initiatives at enterprise scale Methods for breaking down organizational data silos that block cross-functional AI adoption in Fortune 100 environments Practical approaches to calculating and reducing the hidden costs of dirty data that erode AI return on investment AI-Ready Data serves business and technology leaders, including CIOs, CDOs, CTOs, and CISOs, as well as data professionals responsible for building and maintaining the data infrastructure behind AI initiatives. It delivers actionable frameworks for resolving data quality, governance, and architecture challenges that directly affect AI system performance.
Build scalable AI systems by fixing data silos, quality decay, and governance gaps AI-Ready Data provides a structured roadmap for organizations deploying traditional AI, large language models, and agentic AI systems. Written by Andrew Madson, who has held data strategy leadership roles at Fortune 100 companies including JPMorganChase and MassMutual, the book focuses on the foundational data challenges that undermine AI outcomes. It connects AI engineering, data strategy, and infrastructure planning into a unified approach for building production-grade AI systems. The book details how to identify and resolve data silos, quality decay, and governance gaps that create hidden costs and erode AI ROI. It covers modern data product architectures and compliance-ready systems designed to accelerate model deployment and reduce technical debt. Each chapter addresses specific operational pain points, from dirty data remediation to building scalable infrastructure that supports traditional ML pipelines, LLM integration, and agentic AI workflows. Readers will also find: Strategies for diagnosing and eliminating data quality decay across enterprise data pipelines before it undermines AI model performance Frameworks for building modern data products and architectures that reduce technical debt and accelerate model deployment cycles Governance models designed to close compliance gaps and create audit-ready systems for AI initiatives at enterprise scale Methods for breaking down organizational data silos that block cross-functional AI adoption in Fortune 100 environments Practical approaches to calculating and reducing the hidden costs of dirty data that erode AI return on investment AI-Ready Data serves business and technology leaders, including CIOs, CDOs, CTOs, and CISOs, as well as data professionals responsible for building and maintaining the data infrastructure behind AI initiatives. It delivers actionable frameworks for resolving data quality, governance, and architecture challenges that directly affect AI system performance.
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