What does this AWS Solution do?

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.

AWS Solution overview

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.

machine-learning-for-telecommunication-architecture
 Click to enlarge

Machine Learning for Telecommunication solution architecture

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.

The solution ingests data from the Amazon S3 bucket into the Amazon SageMaker cluster and runs the Jupyter notebooks on the dataset.

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.

Machine Learning for Telecommunication

Version 1.0
Last updated: 11/2018
Author: AWS

Estimated deployment time: 5 min

Source code  CloudFormation template 

Features

Machine Learning for Telecommunication reference implementation

Leverage the Machine Learning for Telecommunication solution out of-the-box, or as a reference implementation for building your own machine learning solution.

Synthetic dataset for training

The solution 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.
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