Debugging AI: Strategies for Upcoming Talents

Debugging AI: Strategies for Upcoming Talents

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Understanding ML System Components

16 of 21

16 of 21

Understanding ML System Components

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Classroom Contents

Debugging AI: Strategies for Upcoming Talents

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

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