Managing AI Projects with Microsoft

Microsoft via Coursera Professional Certificate

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Managing Artificial Intelligence Projects prepares you for AI project management and program management, equipping you to coordinate and deliver AI initiatives with structure and accountability. As organizations expand AI across business operations, they rely on professionals who can guide projects from strategy through execution and production readiness.¹ Designed for project managers and business or technology professionals overseeing AI initiatives, this certificate prepares you to lead AI delivery in the Microsoft Azure AI ecosystem. Prior experience leading projects and familiarity with project management principles and AI/ML terminology are recommended. You will learn how to translate business objectives into structured AI delivery plans, coordinate technical and business stakeholders, and manage AI initiatives across planning, oversight, monitoring, and integration. You will strengthen your ability to maintain alignment across teams, manage risks and timelines, and support responsible AI governance in evolving model and data environments. Through applied scenarios and integrated projects, you’ll practice guiding AI workflows and initiatives from concept to stable production in real enterprise contexts. By completing this program, you’ll strengthen your readiness for AI Project Manager and AI Program Manager roles in enterprise AI environments. ¹ Deloitte Insights, AI adoption in the workforce.

Syllabus

Course 1: Practical AI Strategy and Azure Service Selection
Course 2: Owning the AI Lifecycle in Azure
Course 3: Leading Cross-Functional AI Delivery
Course 4: Running AI as an Enterprise Capability

Courses

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Leading Cross-Functional AI Delivery focuses on managing AI initiatives through structured execution and disciplined coordination. AI projects require close collaboration between data scientists, engineers, architects, legal teams, and business stakeholders. This course equips you to guide that collaboration using practical project management frameworks adapted for AI development. You’ll explore agile methodologies for AI initiatives, resource planning techniques, and structured risk management practices. The course also introduces Azure DevOps as a tool for organizing workstreams and maintaining visibility across teams. You will examine how generative AI tools can support project planning, documentation, and stakeholder communication, improving clarity and efficiency without replacing human oversight. By the end of this course, you’ll be able to manage AI project execution from idea to delivery while maintaining alignment, mitigating risk, and supporting cross-functional coordination in enterprise environments.
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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.
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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.
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Running AI as an Enterprise Capability focuses on sustaining AI initiatives beyond initial deployment. As organizations expand artificial intelligence across business operations, long-term success depends on governance, risk oversight, value measurement, and structured change management. This course explores responsible AI frameworks, security and compliance considerations, and enterprise risk management practices. You’ll examine how to communicate AI strategy clearly to stakeholders, support organizational adoption, and align initiatives with defined business priorities. You will also evaluate AI investments using business value metrics and structured portfolio frameworks to support informed decision-making within established strategic direction. By the end of this course, you’ll be able to contribute to sustainable AI governance programs, support compliance and risk oversight, and coordinate AI initiatives in ways that balance innovation with accountability in enterprise environments.