Explore strategies for debugging AI systems in this 29-minute conference talk from Conf42 Incident Management 2024. Gain insights into the importance of debugging in AI, practical tips for implementation, and methods for continuous improvement in machine learning models. Learn about preventing major errors in AI systems, addressing the high demand for debugging skills, and bridging the gap between academic and practical machine learning. Discover techniques for scaling and training complex models, handling data privacy concerns, and delivering post-training models effectively. Understand the balance between organic content and advertisements, creating diverse timelines, and implementing continuous monitoring with real-world validation. Delve into A/B testing, practical debugging skills for ML systems, and strategies for handling data failures. Acquire debugging techniques tailored for junior engineers and explore the role of mentorship and community in building a supportive culture with effective tooling for AI development.
Overview
Syllabus
Introduction and Session Overview
Importance of Debugging in AI
Practical Tips for Debugging
Continuous Improvement in ML Models
Preventing Big Mistakes in AI Systems
High Demand for Debugging Skills
Gaps Between Academic and Practical ML
Scaling and Training Complex Models
Data Privacy and Sensitivity Filtering
Post-Training Model Delivery
Balancing Engagement: Organic Content vs. Advertisements
Creating a Diverse and Effective Timeline
Continuous Monitoring and Real-World Validation
The Importance of A/B Testing
Practical Debugging Skills for ML Systems
Understanding ML System Components
Handling Data Failures and Their Impact
Debugging Techniques for Junior Engineers
The Role of Mentorship and Community
Building a Supportive Culture and Effective Tooling
Conclusion and Final Thoughts
Taught by
Conf42
