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231014s2024 xx o ||| 0 eng d |
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|a EBLCP
|b eng
|c EBLCP
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|a 9781098151577
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|a 1098151577
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|a (OCoLC)1402815286
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|a UAMI
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| 100 |
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|a Tranquillin, Marco.
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| 245 |
1 |
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|a Architecting Data and Machine Learning Platforms
|h [electronic resource].
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| 260 |
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|a Sebastopol :
|b O'Reilly Media, Incorporated,
|c 2024.
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| 300 |
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|a 1 online resource (362 p.)
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| 500 |
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|a Description based upon print version of record.
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|a Intro -- Copyright -- Table of Contents -- Preface -- Why Do You Need a Cloud Data Platform? -- Who Is This Book For? -- Organization of This Book -- Conventions Used in This Book -- O'Reilly Online Learning -- How to Contact Us -- Acknowledgments -- Chapter 1. Modernizing Your Data Platform: An Introductory Overview -- The Data Lifecycle -- The Journey to Wisdom -- Water Pipes Analogy -- Collect -- Store -- Process/Transform -- Analyze/Visualize -- Activate -- Limitations of Traditional Approaches -- Antipattern: Breaking Down Silos Through ETL -- Antipattern: Centralization of Control
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| 505 |
8 |
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|a Antipattern: Data Marts and Hadoop -- Creating a Unified Analytics Platform -- Cloud Instead of On-Premises -- Drawbacks of Data Marts and Data Lakes -- Convergence of DWHs and Data Lakes -- Hybrid Cloud -- Reasons Why Hybrid Is Necessary -- Challenges of Hybrid Cloud -- Why Hybrid Can Work -- Edge Computing -- Applying AI -- Machine Learning -- Uses of ML -- Why Cloud for AI? -- Cloud Infrastructure -- Democratization -- Real Time -- MLOps -- Core Principles -- Summary -- Chapter 2. Strategic Steps to Innovate with Data -- Step 1: Strategy and Planning -- Strategic Goals -- Identify Stakeholders
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| 505 |
8 |
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|a Change Management -- Step 2: Reduce Total Cost of Ownership by Adopting a Cloud Approach -- Why Cloud Costs Less -- How Much Are the Savings? -- When Does Cloud Help? -- Step 3: Break Down Silos -- Unifying Data Access -- Choosing Storage -- Semantic Layer -- Step 4: Make Decisions in Context Faster -- Batch to Stream -- Contextual Information -- Cost Management -- Step 5: Leapfrog with Packaged AI Solutions -- Predictive Analytics -- Understanding and Generating Unstructured Data -- Personalization -- Packaged Solutions -- Step 6: Operationalize AI-Driven Workflows
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| 505 |
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|a Identifying the Right Balance of Automation and Assistance -- Building a Data Culture -- Populating Your Data Science Team -- Step 7: Product Management for Data -- Applying Product Management Principles to Data -- 1. Understand and Maintain a Map of Data Flows in the Enterprise -- 2. Identify Key Metrics -- 3. Agreed Criteria, Committed Roadmap, and Visionary Backlog -- 4. Build for the Customers You Have -- 5. Don't Shift the Burden of Change Management -- 6. Interview Customers to Discover Their Data Needs -- 7. Whiteboard and Prototype Extensively
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| 505 |
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|a 8. Build Only What Will Be Used Immediately -- 9. Standardize Common Entities and KPIs -- 10. Provide Self-Service Capabilities in Your Data Platform -- Summary -- Chapter 3. Designing Your Data Team -- Classifying Data Processing Organizations -- Data Analysis-Driven Organization -- The Vision -- The Personas -- The Technological Framework -- Data Engineering-Driven Organization -- The Vision -- The Personas -- The Technological Framework -- Data Science-Driven Organization -- The Vision -- The Personas -- The Technological Framework -- Summary -- Chapter 4. A Migration Framework
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| 500 |
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|a Modernize Data Workflows
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| 590 |
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|a O'Reilly
|b O'Reilly Online Learning: Academic/Public Library Edition
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| 655 |
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0 |
|a Electronic books.
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| 700 |
1 |
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|a Lakshmanan, Valliappa.
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| 700 |
1 |
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|a Tekiner, Firat.
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| 776 |
0 |
8 |
|i Print version:
|a Tranquillin, Marco
|t Architecting Data and Machine Learning Platforms
|d Sebastopol : O'Reilly Media, Incorporated,c2024
|z 9781098151614
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| 856 |
4 |
0 |
|u https://learning.oreilly.com/library/view/~/9781098151607/?ar
|z Texto completo (Requiere registro previo con correo institucional)
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| 938 |
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|a ProQuest Ebook Central
|b EBLB
|n EBL30783638
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| 994 |
|
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|a 92
|b IZTAP
|
