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by Ellen Friedman, Ted Dunning
AI and Analytics in Production
Preface
How to Use This Book
1. Is It Production-Ready?
What Does Production Really Mean?
Data and Production
Do You Have the Right Data and Right Question?
Does Your System Fit Your Business?
Scale Is More Than Just Data Volume
Reliability Is a Must
Predictability and Repeatability
Security On-Premises, in Cloud, and Multicloud
Risk Versus Potential: Pressures Change in Production
Should You Separate Development from Production?
Why Multitenancy Matters
Simplicity Is Golden
Flexibility: Are You Ready to Adapt?
Formula for Success
2. Successful Habits for Production
Build a Global Data Fabric
Edge Computing
Data Fabric Versus Data Lake
Understand Why the Data Platform Matters
Capabilities and Traits Required by the Data Platform
Orchestrate Containers with Kubernetes
Extend Applications to Clouds and Edges
Use Streaming Architecture and Streaming Microservices
Cultivate a Production-Ready Culture
DataOps
Making Room for Innovation
Remember: IT Does Not Have a Magic Wand
Putting It All Together: Common Questions
Can You Manage End-to-End Workloads?
How Do You Migrate from Test to Production?
Can You Find Bottlenecks?
3. Artificial Intelligence and Machine Learning in Production
What Matters Most for AI and Machine Learning in Production?
Getting Real Value from AI and Machine Learning
Data at Different Stages
The Life Cycle of Machine Learning Models
Specialized Hardware: GPUs
Social and Teams
Methods to Manage AI and Machine Learning Logistics
Rendezvous Architecture
Other Systems for Managing Machine Learning
4. Example Data Platform: MapR
A First Look at MapR: Access, Global Namespace, and Multitenancy
Geo-Distribution and a Global Data Fabric
Implications for Streaming
How This Works: Core MapR Technology
Comparison with Hadoop
Beyond Files: Tables, Streams, Audits, and Object Tiering
MapR DB Tables
Message Streams
Auditing
Object Tiering
5. Design Patterns
Internet of Things Data Web
Locking Down the Data Link
Dashboards For All or For Each
Data Warehouse Optimization
Extending to a Data Hub
Stream-Based Global Log Processing
Edge Computing
Customer 360
Recommendation Engine
Marketing Optimization
Object Store
Stream of Events as a System of Record
Table Transformation and Percolation
6. Tips and Tricks
Tip #1: Pick One Thing to Do First
Tip #2: Shift Your Thinking
Learn to Delay Decisions
Save More Data
Rethink How Your Deployment Systems Work
Tip #3: Start Conservatively but Plan to Expand
Tip #4 Dealing with Data in Production
Tip #5: Monitor for Changes in the World and Your Data
Tip #6: Be Realistic About Hardware and Network Quality
Tip #7: Explore New Data Formats
Tip #8: Read Our Other Books (Really!)
A. Appendix
Additional Resources
Selected O’Reilly Publications by Ted Dunning and Ellen Friedman
O’Reilly Publication by Ellen Friedman and Kostas Tzoumas
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