Safe Evaluation and Rollout of AI Models

Overview

Syllabus

• Introduction to Safe AI Deployment
Overview of AI model deployment challenges
Importance of safety and reliability in AI systems
Key concepts: regressions, fixes, rollbacks
• Measuring AI Model Performance
Setting performance benchmarks
Evaluation metrics: precision, recall, F1-score, etc.
Handling diverse user inputs and edge cases
• Methods for Safe Evaluation
A/B testing and controlled rollouts
Shadow testing and canary releases
Monitoring and alert systems
• Regression Detection and Management
Automated regression testing approaches
Root cause analysis for regressions
Strategies for quick rollback and mitigation
• Tools and Frameworks
Overview of existing tools for model evaluation and monitoring
Best practices for integrating these tools into production pipelines
• Case Studies
Real-world examples of effective AI model rollouts
Lessons learned from deployment failures and corrective measures
• Future Trends in AI Model Deployment
Advances in deployment automation
Evolving best practices with emerging technologies
• Conclusion and Final Project
Summary of key learnings
Project: Design a safe deployment plan for an AI model using acquired knowledge.

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Subjects

Computer Science