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. Should AI and Analytics at Scale Be Difficult?
- Data Challenges at Scale
- What Makes Production Break?
- What Does Scale Really Mean?
- Scalability Is as Important as Scale
- Scale Up Without Scaling IT
- Look for Faulty Assumptions
- The Shape of the Solution
2. Scale-Efficient AI and Analytics
Comprehensive Data Strategy
- What Is a Comprehensive Data Strategy?
- Counter Example: A Nonscale-Efficient Approach
- Universal Data Access
- Real-World Example: Data Warehouse Off-Load
- Real-World Example: Good Data Infrastructure Simplifies Steps
- Use Positive Incentives to Avoid Data Silos
- What Is in a Data Guarantee?
- Real-World example: Proving Data Guarantees
- Leaning Forward, but Looking Back
- Containerized Computation with Kubernetes
- Separation of Concerns
- Plan for Scalability, Not Just Scale
- Multiuse and Multitenancy: Winning Strategies at Scale
- Comprehensive Data Strategy
3. AI and Analytics Together
- Why AI and Analytics Together?
Challenges and Solutions
- Flexibility Through Open Data Access
- Real-World Example: Traditional and Modern Tools Together
- The Coattail Tactic
- Real-World Example: Real-Time Response Through AI
- Real-World Example: Analytics in Production Plus AI in Development
- Containers and Kubernetes for Isolation and Customization
- Real-World Example: Containerization Improved AI Pipelines
- Data Versioning: Platform-Level Capabilities
- DataOps and IT: Bigger Impact, Less Effort
- Edge and AI Often Co-occur
- 4. AI and Analytics in Edge Systems
- 5. Example Data Infrastructure: HPE Ezmeral Data Fabric
- 6. Where to Go from Here
- Additional Resources
- Title: AI and Analytics at Scale
- Release date: January 2021
- Publisher(s): O'Reilly Media, Inc.
- ISBN: 9781492094371
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