“I know cloud computing.”
It sounds good on a resume.
But there’s a question that matters much more:
What have you actually built on the cloud?
Knowing the difference between IaaS, PaaS and SaaS is useful. Knowing the names of dozens of AWS or Azure services can help too.
But cloud computing starts making much more sense when you deploy something, break something, secure something and figure out why your bill suddenly increased.
If you're learning cloud computing, here are five practical things I think you should build.
- Deploy a Real Web Application
Start simple.
Take an application you've built locally and put it online.
Your architecture might initially look like:
User
↓
Domain
↓
Web Server
↓
Application
Sounds easy.
Then the questions begin.
Where should the application run?
How will users access it securely?
Where do environment variables go?
How do you configure HTTPS?
What happens when the server restarts?
How do you deploy an update?
Suddenly, concepts such as compute, DNS, networking, ports, firewalls and SSL/TLS aren't just definitions anymore.
They have a purpose.
Whether you're learning AWS, Microsoft Azure or Google Cloud, deploying your first real application is one of the best ways to understand what cloud infrastructure actually does.
- Connect Your Application to a Managed Database
Now make the application useful.
Give it data.
Instead of installing a database manually on the same server, experiment with a managed database service.
Your architecture becomes:
User
↓
Application
↓
API
↓
Managed Database
And now you have another set of questions.
Should the database be publicly accessible?
Hopefully not.
How should the application authenticate?
Where should credentials be stored?
What happens if the database fails?
Are backups enabled?
Who has permission to access it?
This is where you begin understanding that cloud engineering isn't simply about running servers somewhere else.
It's about designing systems.
- Build a Serverless Automation
Next, try building something without maintaining a traditional server.
For example:
A user uploads a file.
That upload triggers a function.
The function processes the file.
The result gets stored somewhere else.
File Upload
↓
Cloud Storage
↓
Event Trigger
↓
Serverless Function
↓
Process Data
↓
Store Result
This introduces you to event-driven architecture.
On different cloud platforms, you'll encounter services designed for these kinds of workflows.
The important thing isn't memorizing every service name.
Understand the pattern:
Event → Function → Action
Once you understand the architecture, learning the equivalent service on another cloud platform becomes much easier.
- Deploy an AI Application
Now let's make it more interesting.
Suppose you've built an AI assistant locally.
It might work perfectly on your laptop.
But what happens when other people need to use it?
You might eventually need something like:
User
↓
Frontend
↓
API
↓
AI / LLM
↓
Database or Vector Store
↓
Cloud Storage
↓
Monitoring
Now AI engineering and cloud engineering start meeting.
You have to think about:
API management
Authentication
Secrets
Storage
Databases
Model/API latency
Logging
Scaling
Monitoring
Cost
This is an important transition.
“I built an AI model.”
becomes:
“I built an AI system people can actually access.”
That's a completely different engineering challenge.
- Build Something Secure Enough to Fail Safely
This is the project many beginners skip.
They build something.
It works.
And they stop.
But ask yourself:
What happens when something goes wrong?
Imagine your architecture looks like this:
┌──────────────┐
│ User │
└──────┬───────┘
↓
┌──────────────┐
│ Web App │
└──────┬───────┘
↓
┌──────────────┐
│ API │
└──────┬───────┘
↓
┌───────────┴───────────┐
↓ ↓
┌──────────┐ ┌──────────┐
│ Database │ │ Storage │
└──────────┘ └──────────┘
↓
┌──────────────┐
│ Monitoring │
└──────────────┘
Now start attacking your own assumptions.
What if someone discovers an API endpoint?
What if a secret gets committed to GitHub?
What if traffic suddenly increases 100×?
What if the database becomes unavailable?
What if a developer accidentally receives administrator permissions?
What if logs contain sensitive information?
What if a cloud resource keeps running for a month because nobody noticed it?
This is where concepts such as IAM, least privilege, secrets management, monitoring, logging, backups and cost controls stop being boring cloud terminology.
They become necessary.
The Biggest Cloud Learning Mistake
One mistake I see beginners make is trying to memorize an entire cloud platform.
They open AWS or Azure and see hundreds of services.
Then they think:
“Do I need to learn all of these?”
No.
Start with the problems.
Need somewhere to run code?
Learn compute.
Need persistent data?
Learn databases.
Need files?
Learn object storage.
Need controlled access?
Learn IAM.
Need to understand failures?
Learn monitoring and logging.
Need automatic execution?
Learn serverless.
Need repeatable infrastructure?
Start exploring Infrastructure as Code.
Problem first. Service second.
That mindset makes cloud computing much easier to understand.
AWS vs Azure vs Google Cloud: Which One Should You Learn?
Beginners often spend too much time on this question.
The platforms are different, but many fundamental ideas transfer.
Compute
Networking
Storage
Databases
Identity
Security
Monitoring
Automation
Containers
Serverless
Learn these concepts properly on one platform first.
Then understanding another cloud becomes much easier.
Don't try to become:
“Someone who remembers 150 AWS services.”
Try to become:
“Someone who understands how cloud systems are designed.”
That's a much more transferable skill.
Build a Cloud Portfolio, Not Just a Certificate Folder
If you're learning cloud computing for your career, your GitHub shouldn't be empty.
Build something you can explain.
Document the architecture.
Add a README.
Explain the security decisions.
Show how deployment works.
Mention what went wrong.
Explain what you would improve.
A small working project you genuinely understand can teach you far more than copying a massive architecture you can't explain.
What We're Trying to Teach at Eduleem
At Eduleem School of Cloud and AI, we work with learners who are trying to move from understanding technology conceptually to actually using it.
And that's an important distinction.
Cloud learning shouldn't end with:
“I know what AWS and Azure are.”
The goal should move toward:
“I can design, deploy, secure and troubleshoot something in the cloud.”
Whether you're learning independently, through documentation, through projects or through structured training, keep building.
The cloud makes much more sense once something you've built is actually running on it.
Final Thought
If you're currently learning cloud computing, don't ask yourself:
“How many cloud services do I know?”
Ask:
“What can I build with the services I understand?”
Deploy one application.
Connect one database.
Create one serverless workflow.
Deploy one AI project.
Secure and monitor one architecture.
You'll probably learn more from debugging those five projects than from memorizing another hundred service names.
Build first. Understand deeper. Then build again.
I'm part of the team at Eduleem School of Cloud and AI, where we focus on practical learning across cloud, AI and modern technology skills. I share these posts to make technical concepts easier for students and developers who are building their careers.
