Introducing Machine Learning for Telecommunication
The Machine Learning for Telecommunicaton solution provides a framework for an end-to-end machine learning (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 Jupyter notebooks for their own use case.
The Machine Learning for Telecommunicaton solution provides a framework for an end-to-end machine learning (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 Jupyter notebooks for their own use case.
The solution deploys 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. To learn more about the Machine Learning for Telecommunication solution, see the solution webpage.
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