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Model Registry to Track Deployed Model Provenance
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Classroom Contents
Delivery of Deep Transformer NLP Models Using MLflow and AWS SageMaker for Enterprise AI
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- 1 Intro
- 2 Presentation Outline
- 3 Sales Engagement Platform (SEP)
- 4 ML/NLP/Al Roles in Enterprise Sales Scenarios
- 5 Implementation Challenges: the Digital Divide
- 6 Dev-Prod Divide
- 7 Dev-Prod Differences
- 8 Arbitrary Uniqueness
- 9 A Use Case: Guided Engagement
- 10 Six Stages of ML Full Life Cycle
- 11 Model Development and Offline Experimentation
- 12 Creating a transformer flavor model
- 13 Saving and Loading Transformer Artifacts
- 14 Productionizing Code and Git Repos
- 15 Flexible Execution Mode
- 16 Models: trained, wrapped, private-wheeled
- 17 Model Registry to Track Deployed Model Provenance
- 18 Conclusions and Future Work
