Security, Integration & Automation

Microsoft via Coursera

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Overview

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Securing and operating hybrid AI infrastructure demands identity-based access controls, end-to-end observability, resilient data pipelines, and intelligent automation. This course builds the skills to own security, integration, and operational decisions across production AI platforms. You'll configure Workload Identity federation and Azure Key Vault to eliminate embedded credentials, build Grafana dashboards for GPU and latency metrics, and automate compliance remediation using Azure Resource Graph. You'll construct Data Factory pipelines with schema drift handling, integrate edge alerts with ServiceNow via Logic Apps, and implement event-driven autoscaling using KEDA and Azure Functions. By the end of this course, you'll define security boundaries for container workloads, set observability and compliance standards, design resilient data pipelines, own integration SLAs, and justify automation strategies with documented ROI. Designed for platform engineers securing, integrating, and automating hybrid AI infrastructure. A solid understanding of cloud security fundamentals and experience with automation tools are expected.

Syllabus

Secure Identities: Configure Workload Identity
- This module teaches you to eliminate embedded credentials from container workloads using Azure's identity-based authentication. You'll configure Workload Identity federation, enable credential-free Azure Container Registry (ACR) pulls, and verify secure authentication flows.
Secure Identities: Define Shared Responsibility
- 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.
Monitor & Remediate: Build Observability Dashboards
- 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.
Monitor & Remediate: Automate Compliance Remediation
- 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.
Data Pipelines: Configure Hybrid Data Ingestion
- 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.
Data Pipelines: Handle Schema Drift
- 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.
Logic Apps: Build Alert-to-Incident Workflows
- 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.
Microsoft Logic Apps: Tune Connector Performance
- 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.
Automation and ROI: Build Event-Driven Auto-Scaling
- 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.
Automation and ROI: Evaluate Automation Approaches
- 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.
Project Module: Secure Integration Architecture
- 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.