Predicting and Preventing System Outages - Chaos Engineering 2025

Overview

Explore chaos engineering principles for AI systems to predict and prevent outages through controlled experiments, adversarial attacks, and failure simulations that strengthen system resilience.

Syllabus

• 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

Subjects

Computer Science