A single authentication service hiccup lasting 30 seconds cascaded through an entire AI platform for three hours, costing millions in revenue—all because engineering teams hadn't mapped their service dependencies or implemented systematic resilience practices.
Architect Resilient Microservices for AI Success
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Architect Resilient Microservices for AI Success
本课程是 AI Systems Reliability & Security 专项课程 的一部分
位教师：Harshita Gulati
包含在 中
您将学到什么
Proactive failure analysis builds anti-fragile systems that improve under stress instead of collapsing.
Data-driven optimization using RED metrics (Rate, Errors, Duration) drives performance gains and prevents outages.
Standardized microservice templates speed development while ensuring operational consistency and security compliance.
Resilient architecture comes from defining system boundaries, planning for failures, and implementing full observability.
您将获得的技能
- Performance Tuning
- AI Workflows
- Dependency Analysis
- Microservices
- Performance Metric
- System Monitoring
- Failure Analysis
- Service Level
- AI Security
- Application Performance Management
- Site Reliability Engineering
- Failure Mode And Effects Analysis
- Performance Analysis
- Continuous Monitoring
- Middleware
- Distributed Computing
要了解的详细信息
了解顶级公司的员工如何掌握热门技能
积累特定领域的专业知识
本课程是 AI Systems Reliability & Security 专项课程 专项课程的一部分
在注册此课程时，您还会同时注册此专项课程。
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- 获得对主题或工具的基础理解
- 通过实践项目培养工作相关技能
- 获得可共享的职业证书
该课程共有3个模块
Learners will master systematic dependency analysis techniques to identify and prevent cascade failures in AI system architectures. Through hands-on application of FMEA principles and dependency mapping tools, learners will develop the skills to evaluate service relationships, assess failure propagation risks, and implement targeted safeguards that maintain system reliability under stress.
涵盖的内容
2个视频1篇阅读材料1个作业
Learners will develop expertise in RED metrics analysis (Rate, Errors, Duration) to systematically identify performance bottlenecks and prioritize optimization strategies in AI systems. By analyzing real performance data and applying strategic decision-making frameworks, learners will transform observability metrics into actionable improvements that enhance system performance and user experience.
涵盖的内容
3个视频2篇阅读材料2个作业
Learners will design and implement production-ready microservice templates that standardize logging, tracing, and security middleware across AI service ecosystems. Through practical template development exercises, learners will create reusable foundations that accelerate development velocity while ensuring operational consistency and enterprise-grade security standards.
涵盖的内容
3个视频1篇阅读材料3个作业
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