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Post-Training Model Delivery
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Classroom Contents
Debugging AI: Strategies for Upcoming Talents
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- 1 Introduction and Session Overview
- 2 Importance of Debugging in AI
- 3 Practical Tips for Debugging
- 4 Continuous Improvement in ML Models
- 5 Preventing Big Mistakes in AI Systems
- 6 High Demand for Debugging Skills
- 7 Gaps Between Academic and Practical ML
- 8 Scaling and Training Complex Models
- 9 Data Privacy and Sensitivity Filtering
- 10 Post-Training Model Delivery
- 11 Balancing Engagement: Organic Content vs. Advertisements
- 12 Creating a Diverse and Effective Timeline
- 13 Continuous Monitoring and Real-World Validation
- 14 The Importance of A/B Testing
- 15 Practical Debugging Skills for ML Systems
- 16 Understanding ML System Components
- 17 Handling Data Failures and Their Impact
- 18 Debugging Techniques for Junior Engineers
- 19 The Role of Mentorship and Community
- 20 Building a Supportive Culture and Effective Tooling
- 21 Conclusion and Final Thoughts
