- Introduction to Chaos Engineering
Principles and Objectives of Chaos Engineering
History and Evolution in AI Systems
- Understanding System Resilience
Key Concepts and Metrics
Differences Between Robustness, Fault Tolerance, and Resilience
- Designing Controlled Experiments
Basics of Experiment Design
Hypothesis Formulation and Validation
- Tools and Techniques for Chaos Engineering
Overview of Popular Chaos Engineering Tools
Setting Up Chaos Experiments
- Failure Simulations in AI Systems
Types of Failures and Their Simulation
Techniques for Simulating Network, Hardware, and Software Failures
- Adversarial Attacks
Understanding Adversarial Models
Creating and Implementing Adversarial Scenarios
- Predicting System Failures
Machine Learning Techniques for Failure Prediction
Data Collection and Analysis for Predictive Insights
- Mitigating and Preventing Outages
Strategies for Outage Prevention
Designing Self-Healing and Adaptive Systems
- Case Studies and Real-World Applications
Analysis of Notable Chaos Engineering Implementations
Lessons Learned and Best Practices
- Ethics and Best Practices in Chaos Engineering
Ethical Considerations in Simulating Failures
Developing a Responsible Chaos Engineering Strategy
- Group Project and Practical Application
Conducting a Chaos Experiment
Analyzing Results and Improving System Design
- Course Review and Future Directions
Summary of Key Concepts
Emerging Trends and Future in AI System Resilience
- Assessment and Certification
Assignments and Exams
Criteria for Course Completion and Certification
