As AI moves from prototype to production, I’ve learned the real challenge often isn’t the model, it’s everything around it.
In the early stages, it’s easy to get things working with quick scripts and manual steps. But as data grows and use cases multiply, those early wins can start to slow you down. I’ve been there, watching things break not because the model was wrong, but because the system couldn’t keep up.
What’s helped me, and teams I’ve worked with, is approaching infrastructure as something that should grow with you, not ahead of you.
A few lessons I keep coming back to:
Data pipelines break quietly. Without validation and versioning, even a small change upstream can cause big issues later.
Model performance doesn’t fail loudly. It drifts, degrades, and eventually stops delivering value, often without clear signals unless you’ve built for observability.
Monoliths might be fast at first, but they rarely scale well. Separating workloads (like training vs. inference) gives you more flexibility and fewer bottlenecks.
Build vs. Buy is a spectrum. At early stages, managed tools help you move faster. Over time, owning the right parts becomes more strategic. Most of the time, it’s a mix.
I don’t think there’s one right answer, just better fits for where you are. What’s worked well is starting small, staying curious, and designing for change.
One thing I try to remember: good infrastructure doesn’t need to be loud, it just needs to hold up when it matters.
If you’re thinking about this too, I’d love to learn what you’re building. Always open to trade notes or swap lessons.
