From My Laptop to the Cloud: Why I Started Learning AWS
My journey from building Python and AI projects on my laptop to exploring how cloud computing can take those projects further. Sharing what I’ve learned so far while starting my AWS journey.
As an AI and Data Science student, most of the projects I have worked on so far have started on my laptop. I write Python code, work with datasets, train models, test the results, and that's usually where the project ends.
But recently I started thinking about something different:
What happens when my project needs to run somewhere other than my laptop?
That question is what made me start exploring AWS.
Starting With the Basics
When I first opened AWS, I was honestly a little confused by the number of services available. There are services for computing, storage, databases, networking, security, monitoring, and much more.
Instead of trying to learn everything at once, I decided to start with a few services and understand what problem each one actually solves.
For example:
- EC2 can provide computing resources for an application.
- S3 can be used to store files and datasets.
- RDS can help manage relational databases.
- IAM controls who can access AWS resources.
- Lambda allows code to run without managing a traditional server.
Learning them this way made much more sense to me than simply memorizing service names.
Connecting AWS With AI and Data Science
This is the part that interests me the most.
In a typical machine learning project, we usually focus heavily on the model. But a real application needs much more than the model itself.
You might need:
Data → Storage → Processing → Model → API → Application
And then you also need security, monitoring, and a way to handle more users or data as the project grows.
That's where cloud computing starts becoming interesting for me.
Instead of thinking about AWS as just another technology to add to my resume, I'm starting to see it as something that can help me turn a small project into a usable application.
One Thing I Have Realized
The biggest thing I've learned so far is that learning cloud isn't really about memorizing hundreds of AWS services.
It's about understanding the problem first and then choosing the right service to solve it.
For example, if I need object storage, I should know why S3 makes sense. If I need a virtual server, I should understand why I might choose EC2.
That way, I'm learning the reason behind the technology, not just the technology itself.
What's Next?
I'm currently working toward building my AWS fundamentals and preparing for the AWS Certified Cloud Practitioner certification.
But I don't want my AWS journey to stop with a certificate.
My plan is to take some of the Python and AI projects I've already worked on and gradually experiment with deploying their components on AWS.
I know there will probably be plenty of errors along the way—and that's actually what I'm looking forward to.
For me, learning AWS is about moving from “this works on my laptop” to “this can actually work in the cloud.”
This is just the beginning, but I'm excited to see what I can build from here.
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