If your only motivation for using Google Go is webscraping, and you want to do you ML in python, I would recommend the following stack:

Python requests for scraping data

MongoDB for caching data

pymongo for interfacing python and mongodb

scikit-learn for doing your machine learning

This all happens in python and you can extend it multiple processors with multiprocessing or to multiple nodes with django

If your only motivation for using Google Go is webscraping, and you want to do you ML in python, I would recommend the following stack:

Python requests for scraping data

MongoDB for caching data (MongoDB's page oriented format makes it a natural home for storing JSON objects commonly returned by APIs)

pymongo for interfacing python and mongodb

scikit-learn for doing your machine learning

This all happens in python and you can extend it multiple processors with multiprocessing or to multiple nodes with django