Hybrid Cloud Architecture

Microsoft via Coursera

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Overview

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Hybrid AI infrastructure requires more than basic cloud familiarity—it demands evaluating trade-offs, defending architecture decisions, and enforcing cross-environment governance. This course builds the skills to lead architecture reviews and design hybrid cloud solutions for enterprise AI workloads. You'll apply the Azure Well-Architected Framework to assess multicloud setups, compare hub-and-spoke and Virtual WAN topologies, and provision GPU resources via Terraform with spot-instance cost controls. You'll enforce service mesh security using Istio across AKS, EKS, and GKE, while implementing data sovereignty controls with Azure Policy and Microsoft Purview. By the end of this course, you'll be ready to lead Well-Architected Framework reviews, produce portability assessments, define GPU scaling strategies, and direct data sovereignty policies balancing compliance, latency, and cost. Designed for platform engineers operating hybrid AI infrastructure. You should understand cloud networking (VNets, peering, routing) and have hands-on experience with infrastructure automation tools.

Syllabus

Hybrid Azure: Apply WAF Review
- Conduct systematic architecture reviews using Microsoft's Well-Architected Framework. You'll learn the five pillars, practice identifying issues across each dimension, and produce remediation documentation that stakeholders can act on.
Hybrid Azure: Evaluate Network Topologies
- Analyze and compare hybrid network topologies for AI workloads. You'll build comparison matrices evaluating latency, cost, and scalability, then make evidence-based recommendations for stakeholder review.
Azure Arc & Costs: Onboard Clusters with Arc
- This module teaches you to extend Azure's control plane to Kubernetes clusters running outside Azure. You'll register clusters with Azure Arc, deploy policy and monitoring extensions, and verify unified management across your hybrid infrastructure.
Azure Arc & Costs: Optimize with Advisor
- This module teaches you to identify and act on optimization opportunities for hybrid AI workloads. You'll analyze Azure Advisor recommendations, implement changes, and produce cost-savings reports that demonstrate measurable impact.
Mesh & Portability: Configure mTLS with Istio
- This module teaches you to implement zero-trust networking for microservices using the Istio Service Mesh. You'll configure mutual TLS authentication, verify encrypted communication between services, and validate your security posture meets organizational requirements.
Mesh & Portability: Assess Multicloud Portability
- This module teaches you to assess workload portability across cloud providers. You'll deploy the same AI model to AKS, EKS, and GKE, collect performance data, and produce a portability assessment with platform recommendations.
GPU Terraform: Provision GPU Node Pools
- This module teaches you to provision GPU node pools on AKS using Terraform. You'll create reusable modules with spot instance support and cluster autoscaling, then validate that the infrastructure responds correctly to workload demands.
GPU Terraform: Benchmark Storage Performance
- This module teaches you to benchmark and select optimal storage configurations for GPU training workloads. You'll compare disk types, measure I/O performance, and document your decision with supporting performance data.
Data Sovereignty: Enforce Regional Restrictions
- This module teaches you to implement and verify data residency controls using Azure Policy and Purview. You'll create policies that restrict storage to compliant regions, run compliance scans, and remediate non-conforming resources to achieve audit-ready status.
Data Sovereignty: Evaluate Cloud Deployment Options
- This module teaches you to evaluate deployment options for data with strict sovereignty requirements. You'll compare sovereign clouds, public clouds with regional restrictions, and hybrid approaches, then produce a decision document that balances compliance, latency, and cost trade-offs.
Project Module: Hybrid Architecture Review
- Apply hybrid cloud architecture skills to conduct a focused review of a multicloud AI deployment. Learners assess the architecture across all five WAF pillars, identify the highest-priority risks, and produce a remediation plan suitable for stakeholder presentation.