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AI and Analytics at Scale

Release Date: 2021/01/01
ISBN: 9781492094388
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Getting large-scale data-driven applications for AI and analytics into production doesn't have to be challenging. Technical managers, senior technologists, and implementers today often overlook fundamental aspects of design and data infrastructure—aspects that can make the difference between failed approaches and reliable, successful production systems.

In this exclusive report, you'll learn which practices work—and which don't—at large and innovative companies that have successfully integrated AI and analytics into their workflows. Over the past two years, authors Ted Dunning and Ellen Friedman have worked with a wide range of businesses to deliver in-production systems at a large scale. You'll learn practices that have been particularly beneficial, including many that have been disregarded.

  • Understand why AI is at its best when coupled with analytics
  • Build successful production systems—running AI and analytics on the same infrastructure—at scale with less effort, pressure, and cost
  • Apply aspects of a scale-efficient system, including a comprehensive data strategy, containerization, and scalability without scaling IT
  • Focus on the increasingly popular topics of AI and edge computing
  • Explore an example data infrastructure: HPE Ezmeral Data Fabric

Table of Contents

  1. Preface
  2. 1. Should AI and Analytics at Scale Be Difficult?
    1. Data Challenges at Scale
    2. Effective Data Storage
    3. Data, Kubernetes, and Containerized Applications
    4. In-Production Data from the Start
    5. Flexibility of Data Access
    6. What Makes Production Break?
    7. Security at Scale
    8. What Does Scale Really Mean?
    9. Scale in Terms of Data Size
    10. Scale in Terms of the Number of Files or Other Objects
    11. Scale Can Mean Many Applications or Many Teams
    12. Scale in Terms of Geo-distributed Locations
    13. Scalability Is as Important as Scale
    14. Proliferation as a Symptom
    15. Scale Up Without Scaling IT
    16. Look for Faulty Assumptions
    17. The Shape of the Solution
  3. 2. Scale-Efficient AI and Analytics
    1. Comprehensive Data Strategy
    2. What Is a Comprehensive Data Strategy?
    3. Counter Example: A Nonscale-Efficient Approach
    4. Universal Data Access
    5. Real-World Example: Data Warehouse Off-Load
    6. Real-World Example: Good Data Infrastructure Simplifies Steps
    7. Use Positive Incentives to Avoid Data Silos
    8. What Is in a Data Guarantee?
    9. Real-World example: Proving Data Guarantees
    10. Leaning Forward, but Looking Back
    11. Containerized Computation with Kubernetes
    12. Real-World Example: Containers Enable Smaller Footprint and Less IT
    13. Dealing with Data in a Kubernetes World
    14. Keep Legacy Applications from Weighing You Down
    15. Separation of Concerns
    16. Platform-Level Policies and Actions
    17. Real-World Example: Provisioning a New Data Center
    18. Self-Service Data and Application Management
    19. Real-World Example: Data Motion at Platform Level
    20. Plan for Scalability, Not Just Scale
    21. Growth Without Having to Re-architect Your System
    22. Stampede on Successful Data Infrastructure
    23. Real-World Example: Rapid Growth in User Demand
    24. Growth Without a Requirement to Scale IT
    25. Real-World Example: Responding to Seasonal Spikes
    26. Flexibility to Deal with Temporary Scale
    27. Multiuse and Multitenancy: Winning Strategies at Scale
    28. Who’s on the Other Side of the Wall?
    29. Collocate Work Based on Data, Not on Organizational Structure
    30. Real-World Example: Lunchroom Collaboration Yields Millions
    31. Real-World Example: Reuse Data Without Proliferation
    32. Real-World Example: Reuse Disaster Recovery Facilities
    33. Stepping Stones
  4. 3. AI and Analytics Together
    1. Why AI and Analytics Together?
    2. Second-Project Advantage
    3. Handling Logistics Efficiently
    4. A Human Advantage
    5. Real-World Example: Identifying Business Value
    6. Challenges and Solutions
    7. Flexibility Through Open Data Access
    8. Real-World Example: Traditional and Modern Tools Together
    9. The Coattail Tactic
    10. Real-World Example: Real-Time Response Through AI
    11. Real-World Example: Analytics in Production Plus AI in Development
    12. Containers and Kubernetes for Isolation and Customization
    13. Real-World Example: Containerization Improved AI Pipelines
    14. Data Versioning: Platform-Level Capabilities
    15. DataOps and IT: Bigger Impact, Less Effort
    16. Edge and AI Often Co-occur
  5. 4. AI and Analytics in Edge Systems
    1. Edge Means Many Things
    2. Geo-distribution
    3. Real-World Example: Telemetry Backhaul
    4. High-Volume Ingest
    5. Real-World Example: Autonomous Car Development
    6. Security and Ownership
    7. Edge Management
  6. 5. Example Data Infrastructure: HPE Ezmeral Data Fabric
    1. What Is HPE Ezmeral Data Fabric?
    2. Universal, Multiple-API Data Access
    3. Scalability, Reliability, and Performance
    4. Platform-Level Data Management
    5. Data Movement Without Interference
    6. Conclusion
  7. 6. Where to Go from Here
  8. Additional Resources
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