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Integrate, Scale, and Monitor ML Microservices · LearnSpace
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Integrate, Scale, and Monitor ML Microservices

Курс от Coursera
Средний≈ 3.5 чАнглийский
О курсеНавыкиПрограммаПреподаватели

О курсе

Integrate, Scale, and Monitor ML Microservices” is a hands-on course designed for learners who want to build reliable and scalable machine-learning services. You’ll explore how ML models fit into modern microservice architectures, learning to design clear service boundaries, integrate prediction services effectively, and choose communication patterns that improve resilience. The course also guides you through asynchronous workflows and scaling strategies used in real production systems. Finally, you’ll develop practical troubleshooting skills by interpreting logs, metrics, and distributed traces to diagnose performance issues. Through demos, reflective coach activities, and a realistic analysis project, you’ll gain the technical judgment and practical intuition needed to operate ML microservices with confidence.

Навыки, которые вы освоите

MicroservicesApplication Performance ManagementAI IntegrationsSite Reliability EngineeringAnalysisAI WorkflowsMLOps (Machine Learning Operations)Performance AnalysisContinuous Monitoring

Программа курса

1 модулей · 19 учебных материалов

01Integrate, Scale, and Monitor ML Microservices19 материалов

Integrate ML Microservices into System Architecture

Welcome and Course IntroductionВидеоUnderstanding Your ML Service’s Place in the ArchitectureDIALOGUEFrom Model to Microservice — Designing for IntegrationВидеоService Mesh in MicroservicesЧтение

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Integrate, Scale, and Monitor ML Microservices
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Обучение на Coursera

≈ 3.5 ч

1 модулей

Язык: Английский

Субтитры: Арабский, Французский, Итальянский, Бразильский португальский, Корейский, Немецкий, Индонезийский, Испанский, Японский

Часть программы вашего университета
How ML Microservices Fit Into System ArchitectureВидео
Hands-On Activity: Build & Register a gRPC ML Microservice Задание
Designing Your ML Microservice IntegrationDIALOGUE

Scale ML Microservices with Asynchronous Messaging

Scaling ML Systems with Asynchronous MessagingВидеоKafka Data Pipelines: Best Practices for High-Throughput StreamingЧтениеBuilding a Prediction Queue: Real-World PatternsВидеоScale or Fail: Choosing the Right Communication PatternDIALOGUEHands-On Activity: Build a Kafka Prediction PipelineЗаданиеPractice Quiz: Assessing Async Patterns, Partitioning Choices, and Throughput ReasoningЗадание

Monitor and Maintain ML Microservices with Observability

Observability 101: Logs, Metrics & Tracing for ML MicroservicesВидеоML Observability: The Complete Guide for Modern AI SystemsЧтениеInterpreting Signals: What Are Your Logs, Metrics, and Traces Telling You?DIALOGUEProject: Instrument, Monitor & Analyze Your ML MicroserviceЗаданиеCongratulations and Continuous Learning JourneyВидеоGraded Quiz: ML Microservices Integration & Scaling ChallengeЗадание