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Architecting Data and Machine Learning Platforms
| Autor principal: | |
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| Otros Autores: | , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Sebastopol :
O'Reilly Media, Incorporated,
2024.
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| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
Tabla de Contenidos:
- 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
- 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
- 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
- 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
- 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