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Practical AI Strategy and Azure Service Selection · LearnSpace
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Practical AI Strategy and Azure Service Selection

Курс от Microsoft
Средний≈ 6.4 чАнглийский
О курсеНавыкиПрограммаПреподаватели

О курсе

Practical AI Strategy and Azure Service Selection introduces the structured decision-making required before launching an AI initiative. AI projects often fail due to unclear problem framing or misaligned technology choices. This course helps you build the judgment needed to assess when AI is appropriate and how to align solutions with business objectives. You’ll examine how to map business challenges to AI use cases and evaluate feasibility, risks, and expected value. The course explores the Microsoft Azure AI ecosystem, including Microsoft Foundry, Azure OpenAI Service, and Azure Machine Learning, focusing on capabilities, constraints, and appropriate use-case alignment. By the end of this course, you’ll be able to assess AI opportunities with clarity, support informed service selection decisions, and establish a structured foundation for AI delivery within enterprise environments.

Навыки, которые вы освоите

Business RequirementsGovernanceGoal SettingExpectation ManagementDecision MakingRequirements AnalysisArtificial IntelligenceAI Product StrategyRiskingMicrosoft AzureModel DeploymentCost ManagementResponsible AIAI literacyDiscussion FacilitationBusiness PrioritiesGovernance Risk Management and ComplianceDecision IntelligenceFeasibility StudiesModel Evaluation

Программа курса

5 модулей · 31 учебных материалов

01Avoiding costly AI mistakes early6 материалов
Why AI decisions make or break projectsВидеоUnderstanding the AI approaches teams commonly considerВидеоExploring AI fit in real business situationsDIALOGUEA practical guide to evaluating AI options and alternativesЧтение

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Microsoft

Преподаватель курса

Practical AI Strategy and Azure Service Selection
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Обучение на Coursera

≈ 6.4 ч

5 модулей

Язык: Английский

Часть программы вашего университета
How managers evaluate AI opportunities step by stepВидео
Making smart AI decisions at workЗадание
02Turning business needs into clear AI requirements7 материалов
Using AI to solve the right business problemВидеоTurning business goals into AI requirementsВидеоWriting problem statements for AI workЧтениеTranslating business goals into AI requirementsЗаданиеBusiness problem translation in actionВидеоDefining clear AI requirements in practiceЗадание Making sound AI decisions from business contextЗадание
03Defining what an AI project will and will not do5 материалов
Recognizing when an AI project is ready to proceedВидеоFeasibility signals and Go/No-Go criteria for AI projectsЧтениеFacilitating a Go/No-Go decision discussionDIALOGUEDocumenting an AI commitment decisionЗаданиеEvaluating AI readiness scenariosЗадание
04Microsoft Foundry Governance decisions and workspace design6 материалов
How teams decide AI governance before using the platformВидеоImplementing governance decisions in Microsoft FoundryВидеоA Framework for making AI governance decisionsЧтениеMaking governance decisions for Microsoft Foundry workspacesЗаданиеA worked example of AI workspace governanceВидеоAligning governance decisions for an Microsoft Foundry projectDIALOGUE
05Model deployment and performance optimization7 материалов
What model deployment looks like in real AI projectsЧтениеReviewing a deployed model in Microsoft FoundryЧтениеHow teams deploy models and prepare for optimizationВидеоReasoning through early model deployment decisionsDIALOGUEHow teams compare models and decide what to changeВидеоApplying model deployment and optimization decisions in practiceЗаданиеMaking end-to-end Microsoft Foundry platform decisionsЗадание