Machine Learning for Telecommunication

Machine Learning for Telecommunication deploys a scalable, customizable machine learning (ML) architecture that provides a framework for end-to-end ML workloads for use in telecommunications use cases. This guidance streamlines the process of ad-hoc data exploration, data processing and feature engineering, and machine learning model building including training, evaluation and performing predictions by deploying the model in an endpoint.

The guidance also includes a synthetic telecom IP Data Record (IPDR) dataset to demonstrate how to use ML algorithms to test and train models for predictive analysis in telecommunication. You can use the included Jupyter notebooks as a starting point for doing your own artificial intelligence research to develop your own custom ML models, or you can customize the included notebooks for your own use case.