Build advanced hybrid and multicloud architecture skills for Platform Engineer and Cloud Solutions Architect roles. Learn to design and scale enterprise AI infrastructure across Azure, AWS, GCP, edge, and on-premises environments. This certificate equips you to lead key architectural decisions and manage AI workloads seamlessly across any cloud or local setup.
You'll evaluate infrastructure trade-offs with the Azure Well-Architected Framework, extend management to edge resources via Azure Arc, and defend Kubernetes orchestration strategies. You'll set MLOps standards across data centers, defining drift thresholds, retraining triggers, and promotion gates while building enforcement pipelines.
The program focuses on key decisions: selecting GPU scaling models, defining service mesh security, choosing messaging backbones, and setting hybrid compliance policies. Four hands-on projects yield portfolio artifacts—architecture reviews, runbooks, MLOps pipelines, and compliance strategies—validated with working builds.
By the end of this program, you'll hold the strategies and portfolio artifacts needed to confidently architect, govern, and scale production AI infrastructure anywhere.Designed for engineers with 2+ years of cloud experience managing hybrid setups. Kubernetes knowledge, scripting skills, and cloud experience are required, along with enterprise Azure access (free tier is insufficient).
Applied Learning Project
Throughout this program, you'll complete four hands-on projects that mirror real enterprise scenarios. You'll conduct a Well-Architected Framework review of a multicloud deployment and produce a remediation plan, build an end-to-end edge inference solution with offline capabilities, construct an MLOps pipeline from model training through production deployment, and design a secure integration architecture with automated compliance monitoring. Each project produces portfolio-ready artifacts demonstrating your ability to architect and operate hybrid AI infrastructure at scale.
