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Owning the AI Lifecycle in Azure · LearnSpace
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Owning the AI Lifecycle in Azure

Курс от Microsoft
Средний≈ 13.3 чАнглийский
О курсеНавыкиПрограммаПреподаватели

О курсе

Owning the AI Lifecycle in Azure focuses on managing AI system delivery from build through deployment and ongoing operations. AI initiatives introduce new complexities in data architecture, model development, performance evaluation, and production monitoring. This course equips you to coordinate those moving parts within enterprise environments. You’ll examine cloud-native AI architecture decisions, data readiness requirements, and model development workflows using Azure Machine Learning and Microsoft Foundry models. The course explores how AutoML, generative AI, AI agents, and Copilot deployments fit into structured delivery processes. You will also learn how to interpret model performance metrics, support MLOps practices, and guide production monitoring strategies to ensure AI systems remain reliable and aligned with business objectives. By the end of this course, you’ll be able to coordinate AI delivery across development and operational stages while supporting scalable, production-ready AI systems within the Microsoft Azure ecosystem.

Навыки, которые вы освоите

Data GovernanceData PipelinesMicrosoft AzureAI IntegrationsResponsible AIPerformance MeasurementAI WorkflowsBusiness MetricsAgentic WorkflowsData ArchitecturePerformance AnalysisEnterprise ArchitectureReturn On InvestmentMLOps (Machine Learning Operations)Performance MetricMicrosoft CopilotAI OrchestrationModel DeploymentSolution ArchitectureCloud Management

Программа курса

12 модулей · 72 учебных материалов

01Azure AI service selection and evaluation6 материалов
Understanding your Azure AI service optionsВидеоAzure AI services capabilitiesВидеоAzure AI service selection guideЧтениеAzure AI services tradeoffs and selectionВидео

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Microsoft

Преподаватель курса

Owning the AI Lifecycle in Azure
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Обучение на Coursera

≈ 13.3 ч

12 модулей

Язык: Английский

Часть программы вашего университета
Working through Azure AI service decisionsDIALOGUE
Applying Azure AI service selection criteriaЗадание
02How to make AI architecture decisions6 материалов
How teams design AI architectures that actually workВидеоChoosing the right Azure services for AI projectsВидеоArchitecture decision framework and migration analysisЧтениеHow to decide between cloud and on-premises for AIВидеоWorking through AI architecture decisions with a clientDIALOGUEMaking Azure service choicesЗадание
03Designing and governing data pipelines for AI projects7 материалов
How Azure Data Factory and Purview work togetherВидеоExplore a real Azure Data Factory pipeline ЧтениеHow to build a basic Data Factory pipeline ВидеоUsing Microsoft Purview to govern AI data pipelinesВидеоEvaluate an AI data pipeline design and assess governance readinessЗаданиеMaking data pipeline design and governance decisionsЗаданиеDesigning and governing AI data pipelines in practiceЗадание
04Making model development decisions with AutoML6 материалов
When and why teams use AutoMLВидеоHow teams manage AutoML modelsВидеоInterpreting AutoML results to decide model readinessЗаданиеDeciding between AutoML and custom model developmentЧтениеPracticing AutoML decision makingDIALOGUEApplying the AutoML decision flowЗадание
05Choosing the right AI approach for your project7 материалов
When teams choose fine-tuning vs RAGВидеоAI implementation decision guide for project managersЧтениеHow to write AI requirements that technical teams can executeВидеоHow to evaluate fine-tuning vs RAG and make a recommendationВидеоDesigning and validating an AI implementation planDIALOGUEEvaluating AI implementation options under constraintsЗаданиеMaking model development decsionsЗадание
06Managing AI agent workflows6 материалов
When and why teams deploy AI agents and CopilotsВидеоSetting up agent workflows for business processesВидеоAgent communication protocols (A2A and MCP)ЧтениеHow to diagnose agent workflow failures using logsВидеоDiagnosing agent failures through log analysisЗаданиеAgent workflow design and troubleshootingЗадание
07Building and governing Copilot deployments6 материалов
Microsoft 365 Copilot implementation approachЧтениеEvaluating a Copilot design and conducting a responsible AI reviewВидеоEvaluating a no-code M365 Copilot designЗаданиеComplete AI deployment managementDIALOGUECopilot design and responsible AI assessmentЗаданиеManaging AI agents and Copilot deploymentsЗадание
08Reading AI performance reports for business decisions6 материалов
Understanding key performance metricsВидеоA practical framework for connecting AI metrics to business outcomesЧтениеHow to read AI performance reports for business impactВидеоCreating executive performance reportsВидеоInterpreting AI performance reportsDIALOGUEAnalyzing AI performance for strategic business decisionsЗадание
09Building and operating reliable ML pipelines7 материалов
What Azure ML Pipelines are and how they workВидеоDesigning effective Azure ML Pipelines and diagnosing failuresВидеоProduction AI monitoring and performance frameworksЧтениеConnecting Azure ML Pipelines monitoring and CI workflowsВидеоPracticing how to read Azure ML Pipeline resultsDIALOGUEMaking deployment decisions from MLOps signalsЗаданиеReading AI performance for business decisionsЗадание
10Production monitoring and retraining decisions5 материалов
Early warning signs in production AI systemsВидеоUsing Azure Monitor to decide when to actВидеоHow teams monitor, alert, and decide on model retrainingЧтениеSetting up monitoring and security for production AI systemsВидеоProduction monitoring and retraining decisionsЗадание
11Enterprise integration and access governance5 материалов
Enterprise integration and access decisions in production AIВидеоEnterprise integration security and access governanceЧтениеAuditing and managing enterprise integration accessВидеоInterpreting monitoring and integration signalsDIALOGUEManaging AI performance after deploymentЗадание
12End-to-end AI system delivery project5 материалов
Designing AI systems that actually work in productionВидеоWhat you are building and how it will be evaluatedВидеоAI system delivery project guide and evaluation criteria ЧтениеA practical framework for building an AI system from data to deploymentВидеоCreate a complete AI system delivery planЗадание