Can We Talk About the "AI/ML Engineer" Shortcut for a Second?

Nada Shawer — Fri, 26 Jun 2026 03:42:19 +0000

Lately, it feels like my feed is completely flooded with "Become an AI/ML Engineer in 2 Hours!" crash courses and quick certificates promising a golden fast-track into machine learning roles.

But let’s be completely real for a second: there are no tutorial shortcuts here.

The more I dive into actual system architecture and cloud infrastructure, the more obvious it becomes: machine learning isn't a standalone magic trick. It's built entirely on rock-solid Computer Science, efficient data structures, and heavy-duty software engineering.

Software Engineering First, AI Second

If you can’t build or scale a reliable backend, manage data pipelines, or understand low-level underlying system logic, you simply cannot scale an AI model in production. Prompt engineering is cool for prototyping, but production-level ML requires real, foundational engineering skills. You have to learn how to be a great software engineer first.

Looking Past the Hype (A Solid Structural Roadmap)

If you actually want to look past the superficial fluff and understand how real data workloads, model deployments, and ML infrastructure fit into a cloud environment, I found an incredibly solid, structured resource.

Instead of hand-waving past the hard parts, Microsoft Learn has an official, step-by-step breakdown on Azure AI and Machine Learning Fundamentals. It actually goes into the core architectural principles and shows you what real cloud-scale infrastructure looks like.

Whether you are trying to map out your summer learning roadmap or just want to understand the actual systems backing these models, I highly recommend checking it out.

Here is the structured entry point if you want to skip the shortcuts and dive into the real infrastructure:

🔗 Official Azure Machine Learning Technical Hub

What are your thoughts? Are you seeing the same "AI shortcut" hype on your feeds, or are people finally starting to focus back on core system fundamentals? Let's discuss in the comments!

Building a Second Brain in Obsidian

Nada Shawer — Tue, 02 Jun 2026 17:45:04 +0000

I've spent so much time trying to memorize different things across different technologies. But the truth is? Tech evolves rapidly, and every single day new ways of doing things come up.

So..

The key is learning the why behind the how.

We can always learn the “how” by Googling it or checking the docs, but the “why” is what we, as developers, actually need to understand.

I hit a point where my notes were scattered across docs, screenshots, and random folders. That’s when I realized the problem wasn’t the content, it was the system!

The human brain isn’t built to store everything long-term. Because of that, I started building a “second brain.”

The idea is simple: it’s a folder on your computer that holds your most important architectural deep dives, solutions to recurring bugs, quick reference docs, system designs, and more.

NOT syntax, NOT full code examples, NOT things you can easily find through a quick Google search.

For example..

A diagram explaining how Redis handles persistence
A note on how you debugged a Docker networking issue last month

I personally use Obsidian to take notes, plan my learning, track progress, and organize everything in one place. It’s basically a local folder on your machine that you can customize, securely back up, and access anywhere.

Here’s how I’ve structured my engineering roadmap for the summer to handle the workload:

A dynamic Kanban board to pace weekly milestones
A highly visual, structured Markdown notes that keep core ideas easy to scan
A local knowledge graph that visually links related technical concepts over time

Stop trying to cache everything in your short-term memory. Build a system that helps you think instead.

What’s your system? Are you team Obsidian, Notion, or just raw markdown files? I’m curious how other engineers structure their knowledge!

DEV Community: Nada Shawer

Can We Talk About the "AI/ML Engineer" Shortcut for a Second?

Software Engineering First, AI Second

Looking Past the Hype (A Solid Structural Roadmap)

Building a Second Brain in Obsidian