Madson andrew (29 Ergebnisse)

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Paperback. Zustand: New. 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 performanceFrameworks for building modern data products and architectures that reduce technical debt and accelerate model deployment cyclesGovernance models designed to close compliance gaps and create audit-ready systems for AI initiatives at enterprise scaleMethods for breaking down organizational data silos that block cross-functional AI adoption in Fortune 100 environmentsPractical 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.…

Paperback. Zustand: new. Paperback. 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 performanceFrameworks for building modern data products and architectures that reduce technical debt and accelerate model deployment cyclesGovernance models designed to close compliance gaps and create audit-ready systems for AI initiatives at enterprise scaleMethods for breaking down organizational data silos that block cross-functional AI adoption in Fortune 100 environmentsPractical 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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Zustand: New.

Zustand: New. 2026. 1st Edition. paperback. . . . . .

Zustand: new.

Paperback. Zustand: Brand New. 141 pages. 10.00x8.00x0.34 inches. In Stock. Hackett, Andrew T (illustrator).

Zustand: New.

Zustand: New. 2026. 1st Edition. paperback. . . . . . Books ship from the US and Ireland.

Paperback. Zustand: Brand New. 256 pages. In Stock.

Zustand: New.

Zustand: New.

Zustand: As New. Unread book in perfect condition.

Paperback. Zustand: New. Revolutionize your understanding of modern data management with Apache Polaris (incubating), the open source catalog designed for data lakehouse industry standard Apache Iceberg. This comprehensive guide takes you on a journey through the intricacies of Apache Iceberg data lakehouses, highlighting the pivotal role of Iceberg catalogs.Authors Alex Merced, Andrew Madson, and Tomer Shiran explore Apache Polaris's architecture and features in detail, equipping you with the knowledge needed to leverage its full potential. Data engineers, data architects, data scientists, and data analysts will learn how to seamlessly integrate Apache Polaris with popular data tools like Apache Spark, Snowflake, and Dremio to enhance data management capabilities, optimize workflows, and secure datasets.Get a comprehensive introduction to Iceberg data lakehousesUnderstand how catalogs facilitate efficient data management and querying in IcebergExplore Apache Polaris's unique architecture and its powerful featuresDeploy Apache Polaris locally, and deploy managed Apache Polaris from Snowflake and DremioPerform basic table operations on Apache Spark, Snowflake, and Dremio.…

Zustand: New.

Zustand: new.

Paperback. Zustand: New. Revolutionize your understanding of modern data management with Apache Polaris (incubating), the open source catalog designed for data lakehouse industry standard Apache Iceberg. This comprehensive guide takes you on a journey through the intricacies of Apache Iceberg data lakehouses, highlighting the pivotal role of Iceberg catalogs.Authors Alex Merced, Andrew Madson, and Tomer Shiran explore Apache Polaris's architecture and features in detail, equipping you with the knowledge needed to leverage its full potential. Data engineers, data architects, data scientists, and data analysts will learn how to seamlessly integrate Apache Polaris with popular data tools like Apache Spark, Snowflake, and Dremio to enhance data management capabilities, optimize workflows, and secure datasets.Get a comprehensive introduction to Iceberg data lakehousesUnderstand how catalogs facilitate efficient data management and querying in IcebergExplore Apache Polaris's unique architecture and its powerful featuresDeploy Apache Polaris locally, and deploy managed Apache Polaris from Snowflake and DremioPerform basic table operations on Apache Spark, Snowflake, and Dremio.…

Zustand: New.

Paperback. Zustand: new. Paperback. 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 performanceFrameworks for building modern data products and architectures that reduce technical debt and accelerate model deployment cyclesGovernance models designed to close compliance gaps and create audit-ready systems for AI initiatives at enterprise scaleMethods for breaking down organizational data silos that block cross-functional AI adoption in Fortune 100 environmentsPractical 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. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.…

Paperback. Zustand: new. Paperback. 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 performanceFrameworks for building modern data products and architectures that reduce technical debt and accelerate model deployment cyclesGovernance models designed to close compliance gaps and create audit-ready systems for AI initiatives at enterprise scaleMethods for breaking down organizational data silos that block cross-functional AI adoption in Fortune 100 environmentsPractical 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. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

Taschenbuch. Zustand: Neu. Neuware - 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.…

Zustand: NEW.

Zustand: As New. Unread book in perfect condition.

Zustand: New.

Paperback. Zustand: New. Revolutionize your understanding of modern data management with Apache Polaris (incubating), the open source catalog designed for data lakehouse industry standard Apache Iceberg. This comprehensive guide takes you on a journey through the intricacies of Apache Iceberg data lakehouses, highlighting the pivotal role of Iceberg catalogs.Authors Alex Merced, Andrew Madson, and Tomer Shiran explore Apache Polaris's architecture and features in detail, equipping you with the knowledge needed to leverage its full potential. Data engineers, data architects, data scientists, and data analysts will learn how to seamlessly integrate Apache Polaris with popular data tools like Apache Spark, Snowflake, and Dremio to enhance data management capabilities, optimize workflows, and secure datasets.Get a comprehensive introduction to Iceberg data lakehousesUnderstand how catalogs facilitate efficient data management and querying in IcebergExplore Apache Polaris's unique architecture and its powerful featuresDeploy Apache Polaris locally, and deploy managed Apache Polaris from Snowflake and DremioPerform basic table operations on Apache Spark, Snowflake, and Dremio.…

Paperback. Zustand: New. 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 performanceFrameworks for building modern data products and architectures that reduce technical debt and accelerate model deployment cyclesGovernance models designed to close compliance gaps and create audit-ready systems for AI initiatives at enterprise scaleMethods for breaking down organizational data silos that block cross-functional AI adoption in Fortune 100 environmentsPractical 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.…

Taschenbuch. Zustand: Neu. AI-Ready Data | Andrew Madson | Taschenbuch | Einband - flex.(Paperback) | Englisch | 2026 | John Wiley & Sons Inc | EAN 9781394371051 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu.

Paperback. Zustand: New. Revolutionize your understanding of modern data management with Apache Polaris (incubating), the open source catalog designed for data lakehouse industry standard Apache Iceberg. This comprehensive guide takes you on a journey through the intricacies of Apache Iceberg data lakehouses, highlighting the pivotal role of Iceberg catalogs.Authors Alex Merced, Andrew Madson, and Tomer Shiran explore Apache Polaris's architecture and features in detail, equipping you with the knowledge needed to leverage its full potential. Data engineers, data architects, data scientists, and data analysts will learn how to seamlessly integrate Apache Polaris with popular data tools like Apache Spark, Snowflake, and Dremio to enhance data management capabilities, optimize workflows, and secure datasets.Get a comprehensive introduction to Iceberg data lakehousesUnderstand how catalogs facilitate efficient data management and querying in IcebergExplore Apache Polaris's unique architecture and its powerful featuresDeploy Apache Polaris locally, and deploy managed Apache Polaris from Snowflake and DremioPerform basic table operations on Apache Spark, Snowflake, and Dremio.…

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