I Kickstarted 2026 With 3 Google Cloud Certifications
A first-year engineering student's guide to learning Cloud, AI/ML, and DevOps in 2 weeks
The beginning:New Year, New Goals
January 2026 started like any other year for most students with ambitious resolutions that often fade by February. But I decided to do something different. As a first-year B.Tech Electrical Engineering student, I wanted to build real technical skills that would set me apart.How I Spent My January: 3 Google Cloud Certifications and a Lot of Coffee
So here's the thing - I'm a first-year EE student and I spent the first two weeks of 2026 doing something I never thought I'd do completing three Google Cloud certification tracks because I was honestly tired of feeling behind. Everyone around me seemed to be learning something cool, building projects, and I was just... existing?
Anyway, I stumbled upon this Gen AI Academy 2.0 program while scrolling through LinkedIn at 2 AM (bad habit I know) and thought "why not?" Worst case I'd quit after a day.
Spoiler: I didn't quit. And now I have three certifications a bunch of new skills and honestly? I feel pretty good about myself.
What even is Gen AI Academy 2.0?
Okay so it's basically this Google Cloud program where you learn by actually doing stuff not just watching videos. They have these different "tracks" you can choose - I picked three because apparently I hate free time.
The tracks I did:
DevOps (automating deployments and stuff)
AI/ML (making computers understand things)
Serverless (running code without managing servers - wild concept)
There are more tracks too but I figured three was enough for someone who still can't properly explain circuit analysis to her parents.
My Learning Journey: 3 Tracks, 3 Perspectives
Track 1: DevOps - Wait, I can automate THAT?
This was actually the first track I completed, and honestly it blew my mind a little.
I learned how to set up CI/CD pipelines (basically automated deployment systems), work with Docker containers, and deploy stuff on Kubernetes. Before this, I thought deployment meant manually uploading files somewhere and praying it works.
The coolest moment? When I set up my first pipeline and watched my code automatically build, test, and deploy itself. I literally just pushed to GitHub and it... happened. No manual work. I may have done a little victory dance in my room.
Also learned about Cloud Build, Artifact Registry, and GKE. None of these words meant anything to me two weeks ago and now I can actually use them without feeling like a fraud.
Real talk though: Kubernetes confused the hell out of me at first. I had to pause watch like three YouTube videos and make my own notes before things clicked. But once it did? Game changer.
Track 2: AI/ML - Making computers smart (or at least trying to)
This track was wild. I got to play with Google's ML APIs without having to understand the insane math behind machine learning (thank god).
Learned about data prep with Dataprep and Dataflow, working with Apache Spark, and using pre-built APIs for:
Natural Language Processing (making sense of text)
Speech-to-Text (voice recognition stuff)
Video Intelligence (analyzing videos)
Document AI (extracting info from documents)
My favorite part? I used the Natural Language API to analyze a movie review I wrote. It told me the sentiment was positive (which duh I loved that movie) but it also pulled out entities and even the emotional tone. It was like having a mini reading comprehension buddy.
The possibilities here are honestly endless. I keep thinking of random projects I could build a sentiment analyzer for Twitter threads, an audio transcription tool, maybe something that extracts data from my terrible handwritten notes.
Reality check: Data preprocessing is NOT as exciting as it sounds. It's necessary, but wow, it can be tedious. Still glad I learned it though.
Track 3: Serverless - Code without the server headache
This one broke my brain a little (in a good way).
The whole concept of serverless is that you write code, deploy it, and don't worry about servers. They just... handle themselves. Auto-scaling, automatic everything. It's kind of magical?
I learned Cloud Run, Cloud Functions, Firebase, Firestore databases, building REST APIs, and automating everything with Cloud Build.
Best moment: I built a function that automatically resizes images when you upload them. No server setup, no infrastructure management. Just code that runs when it needs to and sleeps when it doesn't.
Coming from EE where we literally study physical hardware, the idea of "serverless" felt weird at first. Like, the servers exist somewhere right? But the point is I don't have to deal with them and honestly? That's amazing.
Side note: Firebase is ridiculously easy to use. Like suspiciously easy. I kept waiting for it to get complicated but it just... didn't?
How I actually got through this (without losing my mind)
Look, I'm not gonna lie and say it was easy. It wasn't. But here's what worked for me:
Time management:
I woke up early. Like, 6 AM early. Which is weird for me because I'm definitely a night owl. But I found that if I did 2-3 hours in the morning before classes, my brain was still fresh and I actually retained stuff.
Weekends were my deep work days - 5-6 hours of focused learning. Phone on Do Not Disturb, one browser tab open, just me and the labs.
Learning approach:
Watch the entire tutorial first without touching anything
Then go back and actually do the lab
Take notes in my own words (typing helps me remember)
Screenshot important stuff for later reference
After each module, I'd ask myself: "Okay but what can I actually BUILD with this?"
When I got stuck (which happened A LOT):
Kubernetes? Watched extra YouTube tutorials
Cloud Build YAML files? Made a cheat sheet
API authentication errors? Stack Overflow is my best friend
General frustration? Took a walk, came back fresh
Staying motivated:
Honestly, I just kept reminding myself that each skill badge was one step closer to not feeling like an imposter when talking about tech. Also, spite. When I wanted to quit, I thought about how satisfying it would be to say I actually finished.
(Is spite a valid motivator? Whatever works, right?)
Why this felt different from other online courses
I've started and abandoned approximately 47 online courses (okay, maybe not 47, but a lot) So what made this one stick?
You actually DO things
Every concept had a hands-on lab where you work in a real Google Cloud environment. Not simulations, not fake exercises - actual cloud infrastructure. There's something about doing it for real that makes it stick.
It's relevant RIGHT NOW
These aren't theoretical concepts I'll "need someday." Companies are literally hiring for these skills today. DevOps engineers, ML engineers, cloud developers - all of this is current, in-demand stuff.
You get proof
Each track gives you a skill badge. Not a "participation certificate," but proof you completed specific hands-on challenges. I can share these on LinkedIn, add them to my resume, whatever.
It's free
The quality is genuinely comparable to paid bootcamps, but it's accessible. As a student who's not exactly rolling in money, this matters a lot.
The biggest difference though? I actually finished. That alone feels like a win.
The unexpected stuff I gained
Beyond the technical skills (which, yeah, are cool), I got some things I didn't expect:
Confidence
I'm a first-year EE student and I can now deploy production-level applications. That's kind of insane? Like, I still mess up Ohm's Law sometimes, but I can set up a CI/CD pipeline. Wild.
Clarity about what I like
Turns out, I really enjoy the intersection of AI and automation. I didn't know that before. Now I do, and it's helping me figure out what to focus on going forward.
Proof I can learn hard things
This sounds cheesy, but it's true. When I finished all three tracks, I realized I CAN push through difficult technical material when I commit. That's a good feeling.
Better idea of what I want to do
Most people in first year are still figuring out what specialization to pick, what career path to follow. I'm not 100% sure yet, but I'm way more informed than I was three weeks ago.
Also, I joined some Discord communities and met other people learning the same stuff. Turns out there's a whole community of students doing this, which is pretty cool.
What I'm building next (the scary part)
So here's the thing - learning is great, but if I don't actually BUILD something with these skills, what's the point?
I'm planning three projects over the next couple months:
Project 1: A task manager app (Serverless track)
Using Cloud Run and Firebase to build a real-time task manager. Basically applying everything I learned in the serverless track. Will it be perfect? Probably not. Will it work? That's the goal.
Project 2: Document analyzer (AI/ML track)
Upload a document, extract text, analyze sentiment, get insights. Combining Vision API and Natural Language API. This one actually excites me because I can see myself using it.
Project 3: Auto-deploy portfolio (DevOps track)
My own portfolio website that automatically deploys when I push code to GitHub. Because what better way to prove I learned DevOps than to... actually do DevOps?
Plan is one project every two weeks through February and March. Ambitious? Maybe. But if I could do three cert tracks in two weeks, I can build three projects in two months. Right? Right.
(I'm telling the internet this so I actually have to do it. Accountability and all that.)
If you're thinking about starting...
If you're intimidated:
Start with one track. Seriously. I did three because I'm weirdly competitive with myself, but you absolutely don't need to. Pick whatever interests you most and go from there.
If you're busy with college (same):
One hour a day adds up faster than you think. I know everyone says this, but it's true. 7 hours a week = 30 hours a month = enough to complete a track. It's doable.
If you're not from CS background:
Hi, I'm an EE student. If I can figure this out while simultaneously trying to understand circuit analysis and surviving calculus, you can too. The courses genuinely start from basics.
If you don't know where to start:
Want to build apps? Try Serverless
Love automation? Try DevOps
Fascinated by AI? Try AI/ML
Just pick one and start. You can always do others later.
Real talk: You'll get stuck. You'll be confused. There will be moments where you want to close your laptop and pretend cloud computing doesn't exist. That's normal. Push through those moments. Google things. Watch extra tutorials. Ask questions in forums. It gets better.
Staying Organized:
Notion for tracking progress
Google Keep for quick notes during labs
GitHub to save code snippets and configurations
The Real ROI: What These Skills Are Worth
Let me be honest about why this matters beyond the certificates:
For internships: Companies hiring for cloud/DevOps/ML roles specifically look for hands-on GCP experience. I now have proof I can deliver.
For hackathons: I can now build and deploy full-stack applications with AI features in 24-48 hours. That's a competitive advantage.
For freelancing: These skills are in-demand. I can realistically take on small cloud consulting or deployment projects.
For confidence: Knowing I can learn hard things quickly is worth more than any certificate.
Some questions people have asked me
"How much did it cost?"
Zero rupees. The Gen AI Academy program is free. You get free access to the labs and everything.
"Do you need a good laptop?"
Nope. Everything runs in the cloud. I did this on my regular laptop with decent internet. That's it.
"How technical do you need to be to start?"
Basic programming knowledge helps, but they really do start from fundamentals. If you can write a simple Python script or understand basic code logic, you're good.
"Are these like real certifications?"
They're skill badges from Google Cloud, not professional certifications (those are different and harder). But they show you completed hands-on challenges, which is still valuable. I added them to LinkedIn and my resume.
"Should I do multiple tracks or focus on one?"
Honestly depends on what you want. I did three to get a broad understanding. If you're preparing for a specific job/internship, maybe go deep in one area first. There's no wrong answer.
"Did you really do this in 2 weeks or are you exaggerating?"
I really did. But I also had winter break, so I had more time than usual. During a normal semester, this would probably take a month or more. And that's totally fine.
Let's stay connected
If you're on a similar journey or just want to talk about tech/cloud/student life:
GitHub: https://github.com/padhisneha2025-dev
Email: padhi.sneha2025@gmail.com
I'm figuring this out as I go, so if you want to follow along (or share your own journey), hit me up!
If this was helpful or motivating in any way, feel free to share it. And if you decide to start your own Gen AI Academy journey, let me know - I'd love to hear about it.
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