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ML Lifecycle: Data Train Deploy Manage
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Classroom Contents
The New ML Lifecycle - Deploying and Managing
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- 1 PASS MARATHON
- 2 Technical Assistance
- 3 Infrastructure Management Saps Productivity
- 4 ML Lifecycle: Data Train Deploy Manage
- 5 Challenge: Heterogeneity
- 6 Challenges: Composability
- 7 Challenge: Performance: Cold Start, Throughput, and...
- 8 Solution: Evaluations, Modular Dependencies
- 9 Challenge: Lack of Reusability
- 10 Solution: API Endpoints, Model Repository
- 11 Challenge: Iteration Speed
- 12 Solution: Model versioning
- 13 Challenge: Training vs Production Infrastructure
- 14 Solution: Serverless, Elastic Scaling
- 15 Challenge: Auditability and Governance
- 16 Solution: Chargebacks & Attribution
- 17 Case Study: Financial Services
- 18 ML Infrastructure Best Practices
- 19 ML Infrastructure Components
- 20 Enterprise Considerations
