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24:06 Key Takeaways and Conclusion
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Classroom Contents
Building Scalable MLOps Pipelines That Actually Work
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- 1 00:00 Introduction and Overview
- 2 00:19 Current Challenges in ML Deployment
- 3 01:05 Agenda and Key Topics
- 4 02:16 State of Enterprise ML Ops
- 5 04:27 Maturity Levels in ML Ops
- 6 06:48 Primary Obstacles to ML Success
- 7 08:44 Architectural Patterns for Robust ML Systems
- 8 10:27 Building a Robust ML Ops Pipeline
- 9 12:35 Technical Implementation of Validation Frameworks
- 10 15:31 Continuous Integration and Delivery
- 11 20:54 Cost Optimization Strategies
- 12 22:52 Future Trends in ML Ops
- 13 24:06 Key Takeaways and Conclusion
