It actually depends on the data size, a small tabular data 8 Gb would be sufficient. But a larger one might require more ram

If you train a single model, this shouldn’t be a problem. But using framework like Pycaret would need a bit more ram as it also use parallel processing

I have 16 Gb model with about 6m rows and 10 columns, Pycaret used ~10-15 Gb of ram (yep, it also use swap), but that also depends on what model you are using (SVM use a lot of ram, but LightGBM should be fine)

For the long run, you would eventually off load heavy training task to cloud with team green gpu anyway (cuML and/or RAPIDS). For starters, Colab + gDrive is fine, but a dedicate compute engine is a lot more convenient