This module teaches you to analyze and document security responsibility boundaries for hybrid AI infrastructure. You'll evaluate how responsibility shifts when moving graphics processing unit (GPU) workloads to the cloud and produce a shared-responsibility matrix for stakeholder alignment.

This module teaches you to build unified observability dashboards for hybrid AI infrastructure. You'll configure Log Analytics data collection, write Kusto Query Language (KQL) queries for AI-specific metrics, and create Grafana dashboards with operational alert thresholds.

This module teaches you to identify compliance violations at scale using Azure Resource Graph and automate remediation with Azure Automation runbooks. You'll write queries to find non-compliant resources and build runbooks that fix issues automatically.

This module teaches you to build data ingestion pipelines that connect on-premises and edge data sources to Azure Data Lake. You'll configure a self-hosted integration runtime for hybrid connectivity and create pipelines that transform data to parquet format for efficient machine-learning consumption.

This module teaches you to build resilient pipelines that handle schema changes gracefully. You'll detect schema drift, implement mapping data flows for automatic correction, and verify pipelines continue operating when source schemas evolve.

This module teaches you to build automated workflows that bridge AI infrastructure alerts to enterprise IT systems. ServiceNow is the reference ITSM target used in this course; if the client uses another approved ITSM platform, substitute its supported connector or API and equivalent incident schema. You'll create Logic Apps that receive edge alerts, transform data, and create ITSM incidents within service-level agreement (SLA) requirements.

This module teaches you to optimize Microsoft Logic Apps connector performance for reliability at scale. You'll analyze connector metrics, tune retry policies, and verify reduced failure rates under realistic load conditions.

This module teaches you to build reactive automation using Azure Functions and KEDA (Kubernetes Event-Driven Autoscaling). You'll create automation that responds to queue-depth events and triggers scaling actions on edge inference services, enabling demand-responsive infrastructure.

This module teaches you to evaluate automation approaches on total cost of ownership. You'll compare managed declarative automation (KEDA + infrastructure as code) with custom event-driven code, calculate return on investment for each, and produce a recommendation with an implementation strategy.

Apply your security, integration, and automation skills to design a comprehensive operational architecture for a hybrid AI infrastructure. You'll integrate identity management, observability, data pipelines, and automated remediation into a cohesive system with documented procedures and compliance controls.