Build a framework for an end-to-end machine learning process including ad-hoc data exploration, data processing and feature engineering, and model training and evaluation - Machine Learning for Telecommunication

Build a framework for an end-to-end machine learning process including ad-hoc data exploration, data processing and feature engineering, and model training and evaluation

November 2018 (last update: December 2019)

This implementation guide discusses architectural considerations and configuration steps for deploying the Machine Learning for Telecommunication solution on the Amazon Web Services (AWS) Cloud. It includes links to an AWS CloudFormation template that launches, configures, and runs the AWS services required to deploy this solution on AWS, using AWS best practices for security and availability.

The guide is intended for data scientists, chief data officers, and data engineers who have practical experience architecting on the AWS Cloud.