Relearning Tech: My Roadmap Into AI, Python, and Fullstack
From a decade in backend systems to building with AI—my plan to upskill and ship end-to-end.
I’ve been working in tech for over a decade, currently in a staff+ role at a FAANG company. My day-to-day revolves around backend engineering—low-level distributed systems, large-scale data storage, and processing architectures.
But lately, I’ve realized something: while I’ve been busy deep in infrastructure, I’ve lost touch with many of the recent advances in tech—especially in AI/ML. I use generative AI constantly in both work and daily life, but as an engineer, I don’t just want to use these tools—I want to understand how they work and build with them.
So, after some research (and a bit of soul-searching), I put together a structured learning plan. Sharing it here both to keep myself accountable and to maybe help anyone on a similar journey.
AI/ML
Phase 1: AI/ML Foundations
Current goal → Get a broad overview of AI/ML before diving deep.
Step 1: Google’s Introduction to Machine Learning – a short pre-requisite for the crash course.
Step 2: Google’s Machine Learning Crash Course – highly recommended across the community as a fast, practical overview. I know this won’t go deep, but that’s fine—the goal is breadth at this stage.
Step 3: Practical Deep Learning for Coders – hands-on, coder-friendly, and often praised as the best entry point for engineers.
With these three, I expect to get a good “map of the territory.” Afterward, I’ll branch into Hugging Face’s excellent ecosystem to explore areas that align with my interests.
Future learning (deeper dive):
Python
Most of my career has been in C/C++ and Java. I’ve dabbled in Python for scripts here and there, but never wrote “complete and real” Python code. That was fine for maintaining existing Python scripts, but in AI/ML, Python is the language.
I don’t want to slog through beginner tutorials, so my plan is:
Use ChatGPT as a personal Python mentor: I give it detailed prompts about my background and ask for expert-level explanations and real-world projects. The interactive feedback loop makes it incredibly effective.
Supplement with resources like:
The goal isn’t just to “know syntax,” but to write idiomatic Python that fits into the ecosystem naturally.
Fullstack
Phase 3: Fullstack Engineering (to Build Things End-to-End)
I don’t just want to learn—I want to ship. That means picking up fullstack development so I can take an idea from prototype to product.
Backend options (still deciding):
Java Spring Boot: Easiest given my Java background, but iteration speed might feel slow.
NodeJS/Express (or whatever’s current in JS): Requires learning JavaScript, but has massive community support.
FastAPI: Attractive since I’ll already be deep into Python.
Frontend (web + mobile):
React / React Native: Huge ecosystem, great resources, but requires learning JS + two frameworks.
Flutter: One framework for both web and mobile, easier transition from Java via Dart, though resources are fewer.
I’ll lean on AI heavily to bootstrap UIs (mockup → code), but I’m realistic: AI isn’t magic. To truly leverage it, I need a solid foundation in frontend myself.
Why I’m Sharing This
This isn’t just a personal study plan—it’s a roadmap for engineers like me who’ve been deep in one area of tech and want to upskill without starting from scratch. The landscape moves fast, and staying relevant means constantly rebuilding your toolkit.
I’ll share updates as I progress—what worked, what didn’t, and maybe even some projects I build along the way.
If you’ve walked a similar path—or have resources, tips, or feedback that could sharpen this plan—I’d love to hear from you.
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