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Written by
Rizky R.
A web developer transitioning into cloud computing.
Written by
Rizky R.
A web developer transitioning into cloud computing.
Look, I never thought I'd be the type to blog about "my journey" into anything. But here we are.
A few weeks ago I got an email confirming my spot in the 14th batch of AWS re/Start. Starts March 1st, 2026. That's about a month away as I'm writing this.
I got into AWS re/Start through Orbit Future Academy. They partner with AWS to run the program—found them while doom-scrolling through career options and figured "why not." Didn't expect to actually get in.
AWS stands for Amazon Web Services. It's cloud computing—renting servers, storage, and services from Amazon instead of buying and maintaining your own hardware.
Companies use it to host websites, run applications, and store data. Tokopedia, Traveloka, and a lot of other major services run on AWS.
Market share: AWS has about 30% of the total cloud market. Microsoft Azure is around 20%, Google Cloud is 13%. So AWS is the biggest player.
A huge part of the internet runs on AWS infrastructure.
I have a Bachelor's in Computer Systems and Networking. Yes, the degree with hardware routers, switches, and subnet mask configurations.
While I appreciate knowing how the underlying networks operate, I found configuring physical network equipment to be a bit tedious for my working style. Debugging firmware quirks or searching for a typo in a massive configuration file often felt slow. I realized I preferred faster iteration cycles, so I pivoted.
For the past three years, I've been doing freelance web development, building projects with React and Node.js. I loved the instant feedback loop. You write code, your browser refreshes automatically with HMR, and you instantly see the results. Just you and your browser.
Lately, the web development landscape has evolved significantly.
It's not the tech stack itself—that's still great—but rather how AI has changed the learning and building process. In the past, you'd encounter an error, dive into Stack Overflow, experiment, break things, and eventually understand the core of the problem.
Nowadays, we have access to Foundation Models—the underlying technology developed by AI companies—which are granted access to various tools to help us accomplish our tasks. While these models are incredibly efficient and have raised the baseline for what can be built in seconds, they also change the learning dynamic. They often remove the "productive struggle" that makes knowledge stick; when you can generate high-quality components and complex architectures instantly, you miss out on the deep comprehension that comes from breaking things and fixing them yourself.
While these tools are fantastic, it made me re-evaluate my focus. I wanted to understand the foundational layer that powers all of these new technologies, rather than just connecting the higher-level pieces.
Since AI is becoming so integral to the future of technology, I wanted to learn about the infrastructure that makes it all possible.
Every AI company, large language model, and new tech startup needs robust compute power, storage, networking, and scaling. All of that backend infrastructure lives in the cloud. As the demand for advanced tech and AI grows, the demand for solid cloud infrastructure scales right alongside it.
I see cloud computing as a fundamental tool. I want to build a deep understanding of these systems so I can effectively build and manage the environments where modern applications live.
Before the program officially kicked off, I spent my time exploring AWS Skill Builder. My approach was:
Now that I'm deep into the program, I've noticed a few things that were different from my initial expectations.
For starters, the program is fully online rather than offline. We have the option to take 3-hour classes in the morning or evening via video meeting. Being remote means less of the typical 8-hour social pressure of a physical classroom, but it also demands a lot more self-discipline to avoid distractions at home.
The sheer volume of material is massive, and honestly, the 3-hour daily meetings aren't enough to cover everything in depth. I find myself doing a lot of self-study before and after class. Because I'm used to self-learning, this workflow fits me fine. However, I've noticed that folks who aren't used to self-studying can sometimes get stuck and fall behind the program's deadlines.
The technical curriculum is heavily guided, which is a double-edged sword. On one hand, you can grind through all the required assignments quickly—I managed to finish mine in about half a month's time. On the other hand, the labs and quizzes allow for multiple retries if you get something wrong. I wish they had tighter knowledge checks to ensure the concepts are truly solidifying, rather than allowing trial-and-error to pass.
There's also a significant focus on soft skills and job readiness. We have about 180 of these courses to get through alongside the AWS material. Honestly, getting through these assignments is a slog. You're forced to watch videos without the ability to scrub through them, and a quiz only unlocks after the video finishes. Everything feels entirely AI-generated—it makes me wonder why they couldn't just provide the material as written notes instead of tedious video formats. It feels so disjointed from the core curriculum that I truly believe it was created by a completely different company (especially since it's all in English).
But there are definitely bright spots! They recently hosted a mid-program sharing session featuring alumni who successfully landed jobs after graduating. Hearing their experiences and seeing the direct outcomes of the program was incredibly motivating and useful.
To officially graduate from the AWS re/Start program, you need to meet a few key requirements: maintaining at least 80% class attendance and submitting all assignments, which includes a whopping 96 quizzes and 69 labs.
Completing these requirements earns you your first milestone: the AWS re/Start Graduate badge. You don't actually have to take an exam for this—it's awarded just for finishing the program. On top of that, you receive an exam voucher for the AWS Certified Cloud Practitioner (CCP) exam. Passing this is what gets you officially certified with a second badge. While these milestones are nice, the badges themselves aren't really my main priority.
The real draw, and essentially the end goal of all this, is the job placement phase. The company running this program partners with a bunch of multinational companies, including IBM, Maybank, the Big 4, Shopee, NTT, and more, to provide job placement offers and guidance. I'm really looking forward to (hopefully) getting a chance to interview with some of them.
I'm actually really enjoying the process of stepping into this new domain. There's something exciting about tackling an intimidating subject. Every lesson learned, every lab completed, and every concept that finally clicks is moving me closer to my goals.
Right now, besides waiting for the program to wrap up and brushing up on the knowledge gaps left by the foundational materials, I'm primarily trying to survive and get through the massive backlog of those AI job readiness assignments.
This article is a living document tracking my transition into the cloud space. I'll continue to update it as I clear my certifications and move forward.