Microsoft Hybrid and Multicloud AI & Edge Infrastructure

Microsoft via Coursera Professional Certificate

Go to class Write review

Details

Go to class

Provider

Coursera Professional Certificate
Pricing

Paid Course
Languages

English
Certificate

Certificate Available
Effort

4 weeks, 10 hours a week
Sessions

Self-Paced
Level

Advanced

Found in

Overview

Google, IBM & Meta Certificates – 40% Off

One Coursera Plus subscription covers most Professional Certificates on Coursera.

Unlock All Certificates

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).

Syllabus

Course 1: Hybrid Cloud Architecture
Course 2: Container & Edge Orchestration
Course 3: AI & Analytics Operations
Course 4: Security, Integration & Automation
Course 5: Launch Your Hybrid Cloud AI Career

Courses

0 reviews

View details

Deploying a model is only the beginning. Keeping it accurate, cost-effective, and production-ready requires end-to-end MLOps workflows spanning training, monitoring, deployment, and storage. This course builds the skills to operationalize machine learning systems at enterprise scale. You'll fine-tune vision models with Azure ML, track experiments via MLflow, and process streaming IoT telemetry using Azure Stream Analytics. You'll build CI/CD pipelines with GitHub Actions to automate model image builds for AKS, and optimize ML artifact storage using Azure Blob Storage lifecycle policies and feature store architecture. By the end of this course, you'll establish lifecycle standards with drift thresholds and retraining triggers, define experiment tracking practices, set latency targets for streaming data, own CI/CD pipeline reliability, and design storage tiering and feature store strategies balancing cost and performance. Designed for platform engineers adding MLOps expertise to their infrastructure skills. A basic understanding of machine learning concepts and experience with Python are expected.
0 reviews

View details

Running AI workloads reliably across data centers, Kubernetes clusters, and resource-constrained edge devices requires more than container basics. This course builds the skills to deploy, optimize, and manage containerized inference services from cloud to edge at scale. You'll package models with Helm, validate rollouts, and trace requests with OpenTelemetry and Jaeger to fix latency outliers. You'll build minimal Docker images, configure edge inference using Azure IoT Edge and ONNX Runtime, implement resilient gRPC communication, and execute zero-downtime Blue-Green deployments on AKS. By the end of this course, you'll be able to define deployment standards for containerized inference services, set container optimization targets, select edge inference architectures and communication protocols, validate resilience under injected failures with Chaos Mesh, and own rollback criteria and SLI thresholds for cloud and edge deployments. This course is designed for platform engineers extending container orchestration skills to edge computing and AI inference workloads. Familiarity with Docker and basic Kubernetes concepts is expected.
0 reviews

View details

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.
0 reviews

View details

Technical expertise alone won't land your next role—you need to communicate your skills clearly and confidently to hiring teams. This course helps you translate your hybrid cloud and AI infrastructure knowledge into a compelling professional narrative for senior-level positions. You'll learn to articulate your experience with cloud architecture, MLOps, container orchestration, and edge infrastructure in the language hiring managers and technical recruiters look for. You'll optimize your resume and LinkedIn profile with industry-relevant keywords, and practice interview scenarios specific to enterprise AI infrastructure roles. By the end of this course, you'll be ready to pursue Platform Engineer, Cloud Solutions Architect, and Hybrid Infrastructure Lead positions at the senior individual contributor and technical leadership level with a polished profile and the confidence to perform in technical interviews. This course is designed for learners completing the Microsoft Hybrid and Multicloud AI & Edge Infrastructure Professional Certificate who are actively seeking their next role.