Read the abstract ➤ https://www.conf42.com/Incident_Management_2024_Sunandan_Barman_Shraddha_Kulkarni_46_debugging_ml_strategies Other sessions at this event ➤ https://www.conf42.com/im2024 Support our mission ➤ https://www.conf42.com/support Join Discord ➤ https://discord.gg/mvHyZzRGaQ Chapters 0:00 Introduction and Session Overview 0:44 Importance of Debugging in AI 1:39 Practical Tips for Debugging 3:34 Continuous Improvement in ML Models 4:05 Preventing Big Mistakes in AI Systems 4:39 High Demand for Debugging Skills 5:17 Gaps Between Academic and Practical ML 7:21 Scaling and Training Complex Models 9:37 Data Privacy and Sensitivity Filtering 12:12 Post-Training Model Delivery 14:18 Balancing Engagement: Organic Content vs. Advertisements 14:52 Creating a Diverse and Effective Timeline 15:22 Continuous Monitoring and Real-World Validation 17:21 The Importance of A/B Testing 19:35 Practical Debugging Skills for ML Systems 19:57 Understanding ML System Components 20:29 Handling Data Failures and Their Impact 21:49 Debugging Techniques for Junior Engineers 26:09 The Role of Mentorship and Community 28:12 Building a Supportive Culture and Effective Tooling 29:08 Conclusion and Final Thoughts