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by David Sweenor, Steven Hillion, Dan Rope,
ML Ops: Operationalizing Data Science
ML Ops: Operationalizing Data Science
An Introduction to ML Ops and Operationalizing Data Science Models
What Is ML Ops?
The ML Ops Pain Point: Time to Deployment
What, Really, Is a Model?
Introducing the Four-Step ML Ops Approach
Build
Data Considerations: Structures and Access
Feature Engineering
Model Testing
Manage
The ML Ops Engineer
Getting Out in Front of Model Proliferation
Auditing, Approvals, and Version Control
Reusing and Repurposing Models from a Centrally Managed Repository
Deploy and Integrate
Where Deploy and Integrate Meet
Business App Development
Monitor
Statistical Metrics
Performance Metrics
Business Metrics and ROI
When Models Drift
Retraining and Remodeling
Monitoring Meets Automation
Case Study: Operationalizing Data Science in the Manufacturing Industry—Digital Twin Models
Case Study: Operationalizing Data Science in the Insurance Industry—Dynamic Pricing Models
Conclusion
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