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 million-user scale. Each module features immersive scenarios where you make critical decisions under pressure.
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Recommended experience
What you'll learn
Apply systematic patching strategies to AI models, ML frameworks, and dependencies while maintaining service availability and model performance.
Conduct blameless post-mortems for AI incidents using structured frameworks to identify root causes, document lessons learned, and prevent recurrence
Set up monitoring, alerts, and recovery to detect and resolve model drift, performance drops, and failures early.
Skills you'll gain
- Incident Management
- Vulnerability Assessments
- Dashboard
- Disaster Recovery
- Problem Management
- DevOps
- AI Security
- Automation
- Dependency Analysis
- MLOps (Machine Learning Operations)
- Model Deployment
- Artificial Intelligence
- System Monitoring
- Sprint Retrospectives
- Patch Management
- Site Reliability Engineering
- Continuous Monitoring
Details to know
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1 assignment
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There are 3 modules in this course
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.
What's included
8 readings
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.
What's included
6 readings
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.
What's included
8 readings1 assignment
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Frequently asked questions
To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.
Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.
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Financial aid available,