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.

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. 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.


The Machine Learning for Telecommunication solution helps you implement a framework for an end-to-end ML process on the AWS Cloud using Jupyter Notebook, an open source web application for creating and sharing live code, equations, visualizations and narrative text. The diagram below presents the architecture you can build in minutes using the solution's implementation guide and accompanying AWS CloudFormation template.

  1. An Amazon Simple Storage Service (Amazon S3) bucket includes a synthetic IP Data Record (IPDR) dataset, an AWS Glue job converts the datasets, and an Amazon SageMaker instance includes Machine Learning (ML) Jupyter Notebooks.
  2. The solution ingests data from the Amazon S3 bucket into the Amazon SageMaker cluster and runs the Jupyter notebooks on the dataset.
  3. The notebooks preprocess the data, extract features, and divide the data into training and testing. Amazon S3 Select reads the Parquet compressed data that was processed by the AWS Glue job.  ML algorithms process the training dataset to develop a model to identify anomalies and predict future anomalies.
Deploy Solution
Implementation Guide

What you'll accomplish:

Deploy the Machine Learning for Telecommunication solution using AWS CloudFormation. The AWS CloudFormation template will automatically launch and configure the components necessary to deploy a scalable, customizable ML architecture on the AWS Cloud.

Implement an end-to-end framework for ML processing that includes pre-trained models and a synthetic IP Data Record (IPDR) dataset to demonstrate how to use machine learning algorithms to test and train models for predictive analysis in telecommunication.

What you'll need before starting:

An AWS account: You will need an AWS account to begin provisioning resources. Sign up for AWS.

Skill level: This solution is intended for data scientists, chief data officers, and data engineers who have practical experience architecting on the AWS Cloud.

Q: Can I upload my own datasets?

To use your own dataset, replace the synthetic data location with the location of your data in Amazon S3. We worked with Ribbon Communications to provide the synthetic datasets for model-training purposes. For more information, see the implementation guide.

Q: Can I modify the Jupyter notebook code?

Yes. The notebook code and features can be modified to create different ML models and related visualizations for your specific use case.

Q: Can I secure my uploaded datasets?

If you choose to use the Jupyter notebooks against your own datasets, we recommend following AWS best practices for uploading data into Amazon S3 to ensure that your data is uploaded quickly and securely. For more information, see the implementation guide.

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