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Cloud Engineering Masterclass: AWS & Azure — Zero to Product
New
8 students
What you'll learn
- Deploy production-ready applications on both AWS and Azure — with proper networking, security groups, load balancers, and auto-scaling configured from scratch
- Write Terraform infrastructure as code that provisions your entire cloud environment — VPCs, compute, databases, and security — in a single command
- Build and manage Kubernetes clusters on EKS and AKS — deploy containerised microservices, configure Helm charts, and set up horizontal pod autoscaling
- Create automated CI/CD pipelines from GitHub to production using GitHub Actions for AWS and Azure DevOps Pipelines — zero manual deployments
- Design cloud architectures that are secure by default — IAM roles, least-privilege policies, KMS encryption, Key Vault secrets, and VPC network segmentation don
- Understand and control your cloud bill — rightsizing instances, reserved capacity, savings plans, tagging governance, and budget alerts that actually work
- Build and deploy serverless applications using Lambda, Azure Functions, API Gateway, SQS, and EventBridge — event-driven architecture that scales automatically
- Integrate AI services into cloud infrastructure — AWS Bedrock, Azure OpenAI, managed vector databases, and a working RAG application deployed end to end
- Set up real observability with Prometheus, Grafana, CloudWatch, and Azure Monitor — dashboards, alerting rules, and log analysis for production systems
- Complete 10 hands-on projects you built yourself — a portfolio that proves your skills in any cloud engineering interview
Explore related topics
Course content
14 sections • 72 lectures • 6h 44m total length
Requirements
- Basic comfort using a terminal or command prompt — you don't need to be a Linux expert, we cover what you need as we go
- Some programming experience in any language — Python, JavaScript, Java — enough to read and write simple scripts. Complete beginners may find a few sections challenging but everything is explained step by step
- A laptop or desktop with internet access — Windows, Mac, or Linux all work fine
- The ability to create a free AWS account and a free Azure account — both have generous free tiers and we walk through setup in the first section, so you won't be charged for anything we do in this course
- No prior cloud experience required — if you know what a server is and roughly why websites need one, you have enough background to start
- No DevOps or Kubernetes experience needed — we build up from first principles in every section
Description
I've been building cloud systems for over a decade. And the single biggest gap I see in engineers who come through cloud certifications is this — they know the services, but they can't connect them into systems that actually work in production.
They can tell you what S3 is. They can't tell you why their Terraform state file got corrupted, how to debug a failing pod in EKS, or why their Lambda function times out under load. That gap is exactly what this course closes.
This course covers AWS and Azure together — because that's how real companies work. Your core infrastructure is probably on AWS. Your enterprise integrations are probably on Azure. Job descriptions in 2026 don't ask for one or the other. They ask for both. So that's what we build.
We start from first principles. If you've never touched a cloud console, that's completely fine. If you're a developer who knows the basics but hasn't written Terraform or set up Kubernetes, this is built exactly for you. By the end you'll have deployed real applications, automated your infrastructure, built CI/CD pipelines, and understood how to keep a cloud environment secure and cost-efficient — not in theory, but in practice.
Every section ends with a hands-on project. Not a toy exercise — something you can open in an interview and walk through confidently. The capstone brings everything together into a production-grade, multi-tier application running on Kubernetes with automated deployments, proper monitoring, and security built in from the start.
A few things this course covers that most cloud courses skip completely:
Cloud cost management. Most engineers have no idea what they're spending until the bill arrives. We cover rightsizing, reserved instances, savings plans, tagging strategies, and budget alerts that actually catch problems before they become expensive.
AI workloads on cloud infrastructure. AWS Bedrock, Azure OpenAI, managed vector databases, and a working RAG application deployed end to end. This is what employers are starting to ask about in 2026 and almost no cloud course covers it yet.
Security beyond IAM checkboxes. Least privilege done properly, secrets management with KMS and Key Vault, network segmentation, and how to audit your own environment before someone else does.
PLC and serverless architecture as a system — not isolated services. Lambda, Azure Functions, API Gateway, EventBridge, SQS — explained together so you understand how they connect, not just what each one does individually.
This course is updated for 2026. Every section reflects what's actually in cloud job descriptions right now based on real market data.
If you want to pass an exam and forget everything a week later, there are faster courses for that. If you want to become a cloud engineer who can walk into a team and contribute from day one — this is the course.
Who this course is for:
- Developers and software engineers who want to add cloud and DevOps skills to their profile and start landing cloud engineering roles in 2026
- IT professionals and system administrators moving from on-premise infrastructure to AWS or Azure and looking for a practical, hands-on transition guide
- Students and career changers targeting their first job in cloud engineering, DevOps, or infrastructure — with no prior cloud experience required
- Backend engineers and data engineers who already deploy applications manually and want to learn Terraform, Kubernetes, and CI/CD to automate everything