What You Need to Know Before
You Start
Starts 7 June 2025 06:16
Ends 7 June 2025
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38 minutes
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Overview
Explore approaches for safely evaluating and rolling out AI models in production systems, focusing on measuring performance across user inputs to detect regressions requiring fixes or rollbacks.
Syllabus
- Introduction to Safe AI Deployment
- Measuring AI Model Performance
- Methods for Safe Evaluation
- Regression Detection and Management
- Tools and Frameworks
- Case Studies
- Future Trends in AI Model Deployment
- Conclusion and Final Project
Overview of AI model deployment challenges
Importance of safety and reliability in AI systems
Key concepts: regressions, fixes, rollbacks
Setting performance benchmarks
Evaluation metrics: precision, recall, F1-score, etc.
Handling diverse user inputs and edge cases
A/B testing and controlled rollouts
Shadow testing and canary releases
Monitoring and alert systems
Automated regression testing approaches
Root cause analysis for regressions
Strategies for quick rollback and mitigation
Overview of existing tools for model evaluation and monitoring
Best practices for integrating these tools into production pipelines
Real-world examples of effective AI model rollouts
Lessons learned from deployment failures and corrective measures
Advances in deployment automation
Evolving best practices with emerging technologies
Summary of key learnings
Project: Design a safe deployment plan for an AI model using acquired knowledge.
Subjects
Computer Science
