Harden AI: Patch and Recover Incidents Fast

Overview

Master the critical skills needed to maintain AI systems in production through this hands-on course designed for DevOps engineers, ML engineers, and SREs. As AI deployments grow more complex, the ability to patch safely, recover from incidents quickly, and maintain operational health becomes essential. Through realistic crisis scenarios, you'll learn systematic patching strategies that minimize downtime, conduct blameless post-mortems that transform failures into knowledge, and build monitoring systems that detect issues before users notice. Work with industry tools like MLflow while practicing with real incident data. You'll tackle challenges like emergency vulnerability patches, investigate mysterious model failures, and design monitoring for a million-user scale. Each module features immersive scenarios where you make critical decisions under pressure. Ideal for DevOps, ML engineers, and SREs managing AI systems in production. Perfect for those seeking to strengthen skills in monitoring, incident response, and reliability, or preparing for senior operations roles. Basic knowledge of AI/ML concepts, familiarity with deployment pipelines, and some experience in incident management are recommended for successful course completion. By course completion, you'll confidently handle production AI incidents, implement preventive measures, and lead operational excellence initiatives. Perfect for professionals managing AI in production or preparing for senior DevOps/SRE roles.

Syllabus

AI System Patching Strategies

Generate systematic patching strategies for AI models and ML frameworks, build comprehensive dependency maps for complex ML systems, and implement staged deployment protocols with canary testing and automated rollback mechanisms.

Incident Review and Root Cause Analysis

Facilitate blameless post-mortem discussions for AI system failures, apply structured root cause analysis frameworks to categorize AI-specific failure patterns, and transform incident knowledge into actionable prevention strategies through organizational learning systems.

Operational Health and Rapid Recovery

Configure AI-specific monitoring dashboards with drift detection and performance metrics, design incident response runbooks with decision trees and escalation paths, and implement automated recovery mechanisms including self-healing systems and intelligent alerting.