I'm a full stack engineer with four years of experience, and I've been feeling a bit overwhelmed by all the buzz around AI lately. It's tempting to just use AI models, but I'd rather be involved in building AI systems on the application layer. Before asking for a roadmap for this transition, I wanted to know if it's actually worth making the switch. What do you think?
6 Answers
The transition can really be worthwhile, especially if you're keen on the mathematical and research aspects—not just the hype. With your solid engineering background, you're already ahead! I recommend starting with Andrew Ng's Machine Learning course. Get familiar with Python and PyTorch, and then focus on a niche like NLP or computer vision. The market is getting crowded with bootcamp grads, so understanding the fundamentals will help you stand out.
As someone who just uses AI APIs in their applications, my two cents are: don't just dive into AI math and stats. Think about the hardware side too, like robotics—that's where things are headed. Just a thought!
When you mention building AI systems, are you talking about: 1) AI applications and automation using API models, or 2) creating systems for training models like data pipelines? I've heard 'AI systems' used to mean both, so just checking!
I totally get what you're saying about the hype. It seems like everyone is labeling themselves an AI expert these days, which can get exhausting. But on the flip side, there's definitely good money in the more traditional aspects of software engineering that are still super important.
I went through a similar switch, starting with a great tutorial I found on Hugging Face about fine-tuning language models. If you’re more into the application side, diving into that could also be useful!
Sounds good! I’ve got a basics in models already, but I’m really interested in the application layer. What do you suggest as the next step?