u/Proud-Gas-8205

My current guess is that it's less about the model itself and more about everything around it.

Anyone can train a model in a notebook. Building an application where data flows through multiple services, exposing the model through APIs, handling failures, storing results, and making the whole pipeline reliable feels like a much more valuable skill.

I've also noticed job descriptions mentioning things like RAG, MLOps, and production AI systems more often, even for junior roles. It makes me wonder if companies now care more about whether you can ship an ML application than whether you can squeeze another 1% accuracy out of a model.

On a different note, I keep thinking there's room for better AI products in the entrepreneurship space. I don't mean another AI business plan generator, but something that actually helps first-time founders make better decisions while they're building.

For those already working in ML, what made the biggest difference when you were interviewing? Was it the complexity of the pipeline, the problem you solved, or simply being able to explain your design decisions clearly?

If you were interviewing a junior backend developer tomorrow, what topics would you absolutely expect them to know?

I've built end-to-end projects with authentication, databases, REST APIs, and cloud services, but I want to focus my preparation on what companies actually ask instead of trying to study everything.

What would be on your checklist?

If you were interviewing a junior backend developer tomorrow, what topics would you absolutely expect them to know?

I've built end-to-end projects with authentication, databases, REST APIs, and cloud services, but I want to focus my preparation on what companies actually ask instead of trying to study everything.

What would be on your checklist?

Instead I ask:

"What product do I want to build?"

The product tells me what I need to learn.

That's how I ended up learning React Native, Node.js, FastAPI, PostgreSQL, PyTorch, and computer vision while building AI applications instead of collecting tutorial certificates.

When I started learning computer vision, I thought getting 90%+ accuracy meant the model was good.

It wasn't.

Most of my dataset belonged to one class, so the model simply learned to predict that class most of the time. The accuracy looked impressive, but the model struggled on the minority classes.

After using data augmentation and class weights, the overall accuracy changed only a little, but the predictions became much more balanced and actually useful.

That changed how I evaluate models. I pay much more attention to precision, recall, F1-score, and the confusion matrix instead of looking at accuracy alone.

What was the first computer vision lesson that surprised you?