Delivery of Deep Transformer NLP Models Using MLflow and AWS SageMaker for Enterprise AI
Databricks via YouTube
Overview
Syllabus
Intro
Presentation Outline
Sales Engagement Platform (SEP)
ML/NLP/Al Roles in Enterprise Sales Scenarios
Implementation Challenges: the Digital Divide
Dev-Prod Divide
Dev-Prod Differences
Arbitrary Uniqueness
A Use Case: Guided Engagement
Six Stages of ML Full Life Cycle
Model Development and Offline Experimentation
Creating a transformer flavor model
Saving and Loading Transformer Artifacts
Productionizing Code and Git Repos
Flexible Execution Mode
Models: trained, wrapped, private-wheeled
Model Registry to Track Deployed Model Provenance
Conclusions and Future Work
Taught by
Databricks