What’s the best way to manage cloud compute for ML workflows?

I want to automate this workflow:

Launch cloud machines with specific Python environments Run data processing or model training (GPU or many CPU cores) Transfer results back to my local machine Tear down the cloud resources to minimize cost

I’m not tied to any specific tools. I have tried coiled but I am looking for other options.

What approaches or stacks have worked well for you?