VPI General Forum

Cloud computing isn’t strictly necessary for machine learning — you can definitely run ML projects on local machines, especially if you’re just learning, prototyping, or working with smaller datasets.

But in real projects, cloud becomes useful pretty quickly. It helps when you need:

more computing power (GPUs/CPUs)

bigger storage for data

training models faster

running systems that many users access at once

So in reality, most teams use a mix: local setup for development and testing, and cloud for heavy training and production.

I’ve also noticed that a lot of time in ML projects isn’t just training models, but managing all the surrounding work (data prep, experiments, reports). That’s where I started using AI agent tools like AtomicBot — mainly to keep these repetitive steps organized and automated so I can focus more on the actual modeling.