Escaping Tutorial Hell

I have a folder on my laptop full of half-finished or undeployed projects.

There’s a chatbot that works 80% of the time. There’s a fraud detection script that runs perfectly in a Jupyter Notebook but has never seen a production server. There are countless "Hello World" implementations of the latest LLM frameworks.

For a long time, I thought this meant I was learning.

But recently, I hit a wall. I realized that while I can make AI work, I didn’t know how to make it matter.

If a CEO asked me, "Is this model safe to deploy to a million users?" I couldn't honestly answer "Yes." If an auditor asked, "Can you reproduce the exact decision this model made three months ago?" I would have to say "No."

That gap—the massive chasm between a working demo and a responsible, production-grade system—is what I am trying to cross.

I realized that the difference between a junior engineer and a leader isn’t just about knowing more syntax. It’s about responsibility.

A junior engineer celebrates when the code runs. A senior engineer worries about what happens when the data drifts, when the dependency updates, or when a bad actor tries to inject a prompt.

I started this blog series, 100 Days of Responsible AI Engineering, to force myself into that senior mindset.

"Responsible" sounds like a buzzword, but to me, it’s a technical constraint. It means building systems that are:

I am not writing this series as a guru on a mountaintop. I am writing it as an engineer in the trenches.

I am using this blog to simulate a high-stakes environment. For the next 100 days (or however long it takes me to do 100 posts), I am holding myself to a standard:

• No toy problems. If it works in a notebook but fails in a container, it’s a failure.
• No hand-waving. "It depends" is not an answer. I have to make a decision and defend the trade-offs.
• Auditability first. If I can't track it, I won't build it.

I’m writing this for myself, to solidify my own growth. But I’m also writing it for every other software engineer who feels the same Imposter Syndrome I do when looking at the complexity of modern MLOps.

I want to move beyond "copy-pasting tutorials" and start engineering systems that I would be proud to put my name on.

This is going to be hard. I’m going to struggle with tools I haven’t used before. I’m going to have to read boring documentation about compliance and governance.

But that’s the work.

Welcome to Day 000. Let’s build something real.