Machine Learning for Telecommunication
Machine Learning for Telecommunication

Overview

Machine learning (ML) helps Amazon Web Services (AWS) customers use historical data to predict future outcomes, which can lead to better business decisions. ML techniques are core to the communications service provider (CSP) industry. CSPs can use ML algorithms to construct and refine mathematical models from their business data, and then use those models to help identify fraudulent use of network services, automate network functions (zero touch), and reduce customer churn.

AWS offers several ML services and tools tailored for a variety of use cases and levels of expertise. However, it can be a challenge to understand the mechanics of model training and tuning, identify relevant data features, design a workflow that can perform complex extraction, transformation, load (ETL) activities, and scale to accommodate large datasets.

To help customers get started with a machine learning workflow for CSP use cases, AWS offers the Machine Learning for Telecommunication solution. This solution uses AWS CloudFormation to deploy a scalable, customizable ML architecture that leverages Amazon SageMaker, a fully managed ML service, and The Jupyter Notebook, an open source web application for creating and sharing live code, equations, visualizations, and narrative text.

Additionally, the solution provides a framework for an end-to-end ML process including ad-hoc data exploration, data processing and feature engineering, and model training and evaluation. It 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. Customers can use the included notebooks as a starting point to develop their own custom ML models, and customize the included notebooks for their own use case.

Cost

You are responsible for the cost of the AWS services used while running this solution. As of the date of publication, the cost for running this solution with default settings in the US East (N. Virginia) Region is approximately $1.21 per hour. This includes charges for Amazon SageMaker, Amazon Simple Storage Service (Amazon S3), and AWS Glue. This estimate does not include variable charges for data processing and data transfer costs.

Prices are subject to change. For full details, see the pricing webpage for each AWS service you will be using in this solution.

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