Why does Machine Learning need a lot of data while one can do statistical inference with a small set of data? I am wondering why Machine Learning needs a lot of data compared to statistical inference. In statistics, we can use a small amount of data for a statistical inference but in Machine Learning, everybody says we need a lot of data. Why does Machine Learning needs tons of more data compared to Statistics Inference?

A typical machine learning model contains thousands to millions of parameters, while statistical modelling is typically limited to a handful parameters.
As a rule of thumb, the minimum an amount of samples you need is proportional to the amount of parameters you want to estimate. So for statistical modelling of a handful of parameters you might only need a hundred samples, while for machine learning with millions of parameters you may need millions of samples.
