Owning the AI Lifecycle in Azure

Overview

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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.

Syllabus

Azure AI service selection and evaluation

This module builds your ability to evaluate and compare Azure AI services as a decision-maker, not as a technical implementer. You'll learn how project context, including business goals, delivery timelines, data constraints, and organizational requirements, shapes which services are viable for a given initiative. By the end of this module, you'll be able to assess service options, identify misalignments between proposals and requirements, and justify selection recommendations to stakeholders with confidence.

How to make AI architecture decisions

This module develops your ability to reason through AI architecture decisions and evaluate trade-offs that shape system design. You'll learn how teams move from business requirements to architectural choices, when specific Azure services are appropriate, and how to assess cloud versus on-premises deployment options. By the end of this module, you'll be able to participate meaningfully in architecture discussions, evaluate proposals against project constraints, and guide teams through decisions that balance performance, cost, security, and operational feasibility

Designing and governing data pipelines for AI projects

This module builds your ability to evaluate data pipeline designs and assess governance readiness for AI projects. You'll learn how Azure Data Factory and Microsoft Purview work together to move data and maintain oversight, how to interpret pipeline structures and governance outputs without configuring them yourself, and how to identify risks related to data lineage, PII classification, and compliance. By the end of this module, you'll be able to review pipeline proposals, assess governance gaps, and guide teams toward designs that meet both delivery and compliance requirements.

Making model development decisions with AutoML

This module builds your ability to use AutoML (Automated Machine Learning) strategically as a decision-making tool rather than treating it as a shortcut for model development. You'll learn when AutoML is appropriate for establishing baselines and testing feasibility, how to interpret AutoML results to assess model readiness, and how to decide when results are "good enough" versus when custom development is warranted. By the end of this module, you'll be able to review AutoML outputs, document defensible recommendations, and guide teams through model development decisions with confidence.

Choosing the right AI approach for your project

This module develops your ability to choose between AI implementation approaches and communicate requirements clearly to technical teams. You'll learn how business constraints, including content volatility, cost sensitivity, compliance exposure, and delivery timelines, shape whether fine-tuning or RAG is appropriate for a given situation. You'll also learn to write structured requirements that technical teams can execute without ambiguity. By the end of this module, you'll be able to evaluate implementation options, justify your recommendations, and translate strategic decisions into actionable specifications.

Managing AI agent workflows

This module builds your ability to oversee AI agent deployments and diagnose workflow issues when they arise. You'll learn when agents are appropriate for automating complete business processes, how agent workflows are structured and where failures typically occur, and how to interpret log information to identify problems and coordinate resolution. By the end of this module, you'll be able to evaluate agent proposals, review workflow designs for risk, and guide troubleshooting conversations with technical teams, without performing technical debugging yourself.

Building and governing Copilot deployments

This module develops your ability to evaluate and govern Copilot deployments within Microsoft 365 environments. You'll learn how to assess no-code Copilot designs for business fit and integration appropriateness, how to conduct Responsible AI reviews that identify fairness, transparency, and accountability concerns, and how to document remediation steps when issues are found. By the end of this module, you'll be able to review Copilot proposals, guide deployment decisions, and ensure AI assistants operate within organizational and ethical guidelines.

Reading AI performance reports for business decisions

This module builds your ability to read AI performance reports and translate technical metrics into business impact. You'll learn what classification metrics like precision, recall, F1-score, and AUROC actually measure, how different metrics reflect different types of business risk, and how to connect performance data to ROI and resource allocation decisions. By the end of this module, you'll be able to review performance reports with confidence, identify when intervention is needed, and communicate findings to executives in terms that drive action.

Building and operating reliable ML pipelines

This module develops your ability to oversee machine learning pipelines and make deployment decisions based on operational signals. You'll learn how Azure ML pipelines structure work across training, validation, and deployment stages, how to interpret pipeline results to identify failures and their likely causes, and how CI/CD practices connect monitoring outcomes to release decisions. By the end of this module, you'll be able to review pipeline status, coordinate resolution when issues arise, and guide teams through deployment decisions that balance delivery speed with operational safety.

Production monitoring and retraining decisions

This module builds your ability to monitor production AI systems and make retraining decisions based on drift and degradation signals. You'll learn how AI systems degrade over time, what monitoring signals indicate emerging problems, and how to decide when investigation, retraining, or continued observation is appropriate. By the end of this module, you'll be able to interpret alerts and dashboard trends, distinguish between noise and meaningful signals, and guide teams through retraining decisions that balance responsiveness with restraint.

Enterprise integration and access governance

This module develops your ability to oversee enterprise integrations for AI systems and ensure they operate securely within organizational boundaries. You'll learn how Copilots and agents connect to enterprise platforms like Microsoft Graph, SharePoint, and Teams, how to evaluate API permission requirements and apply least-privilege principles, and how to audit access over time to identify and remediate overly broad permissions. By the end of this module, you'll be able to assess integration proposals, guide access governance decisions, and coordinate with security teams to maintain secure AI operations.

End-to-end AI system delivery project

This module gives you the opportunity to demonstrate your ability to plan and justify end-to-end AI system delivery in an enterprise environment. You will develop a complete AI system delivery plan that brings together conceptual architecture, operational oversight, governance, and business integration for an AI-enabled decision support system. In your project, you’ll show how data, AI capabilities, workflows, monitoring signals, accountability, and stakeholder communication connect to support reliable business decision-making. By the end of this module, you’ll have produced a structured, business-facing delivery plan that demonstrates system-level reasoning, clear trade-off analysis, and responsible AI project leadership.