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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您将学到什么
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.
您将获得的技能
- Sprint Retrospectives
- MLOps (Machine Learning Operations)
- Continuous Monitoring
- Model Deployment
- Artificial Intelligence
- Site Reliability Engineering
- Problem Management
- Dependency Analysis
- Disaster Recovery
- Patch Management
- System Monitoring
- AI Security
- DevOps
- Automation
- Incident Management
- Vulnerability Assessments
- Dashboard
要了解的详细信息
添加到您的领英档案
1 项作业
了解顶级公司的员工如何掌握热门技能
该课程共有3个模块
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.
涵盖的内容
8篇阅读材料
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.
涵盖的内容
6篇阅读材料
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.
涵盖的内容
8篇阅读材料1个作业
提供方
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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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