Stop guessing whether the bill is fair or the server is exposed — provision, secure, monitor, and price real infrastructure yourself.
You can deploy to one cloud by following a tutorial, but you still cannot tell whether the bill is reasonable, whether the machine is reachable from the open internet, or what happens when a region goes down. This course closes that gap. You start from a bare terminal, install all four command-line tools — AWS, Azure, Google Cloud Platform (GCP), and DigitalOcean — and then provision the same workload on each: virtual machines behind an auto-scaling group, object storage fronted by a content delivery network (CDN), managed Postgres with read replicas and point-in-time restore, and a Virtual Private Cloud (VPC) whose firewall rules genuinely block traffic. Along the way you set up Identity and Access Management (IAM) so every service holds only the permissions it needs, wire up metrics, logs, and alerts, and read a cost breakdown line by line instead of flinching at the total. The last module is governance: Service Control Policies, Azure Policy, and Open Policy Agent rules that reject an untagged, unencrypted database before it exists. The capstone is one three-tier app, live on all four clouds, with a recommendation you can defend.
Built by Lakshya Kumar
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Launch, harden, and image virtual machines, then let a scaling group absorb a traffic spike without anyone opening a console at midnight.
Host a static site, version every object, sign a private download link, and cut latency by putting a cache in front of your bucket.
Add replicas, pooling, and point-in-time restore so a failed primary costs you minutes of downtime instead of a lost weekend.
Model users, roles, and service accounts across four clouds, keep secrets in a vault, and make a leaked key worth almost nothing.
Turn metrics, logs, and traces into alerts and error budgets that name what is failing, not just that something somewhere is.
Move cold data to archive tiers, find the egress charges eating your margin, and sync objects between clouds without a migration project.
Design subnets and route tables, pick between security groups and network ACLs, and reach a private server without exposing it.
Tag everything, find the idle resources nobody remembers creating, and commit only to the discounts your real usage pattern justifies.
Write org-level policy that rejects untagged or unencrypted resources at creation, then audit the whole estate against CIS benchmarks.
Complete all modules, then submit the required number of capstone projects. Each must earn a passing rating from an admin reviewer.
Deploy a resilient three-tier application (load balancer, app servers, managed database) simultaneously on AWS, Azure, GCP, and DigitalOcean using only CLI tooling. Each deployment must use the cloud's native IAM, object storage, and VPC. Build a cost dashboard for each cloud and document a written recommendation: which cloud would you pick for production, and why? The recommendation must cite at least three concrete pricing or feature differences observed during your build.
Paste this into any AI chat. Fill in the bracketed parts with your context — you'll get back a straight answer on whether this belongs on your plate.
I'm considering 'Multi-Cloud Foundations' — the entry course in the DevOps track. Covers compute, storage, networking, IAM, monitoring, cost, and governance across AWS, Azure, GCP, and DigitalOcean. 10 modules × 10 tasks each. Context: 1. My current role: [e.g. "backend dev", "frontend dev who deploys via Vercel", "junior SRE", "founder", "QA"] 2. My cloud experience: [e.g. "none — never used AWS", "AWS only", "Heroku/Vercel only", "deployed once but it scared me"] 3. My goal: [e.g. "deploy my first multi-cloud app", "pass AWS Cloud Practitioner", "save my company money on cloud bills", "prepare for an SRE interview"]
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