Predicting telecom call-drops with AI

This series demonstrates a telecom call-drop scenario using IBM Cloud Pak for Data. The Series provides an end-to-end solution, starting from:

  • Collecting and aggregating data
  • Building and monitoring machine learning models used to predict call drops for a given cell tower
  • Creating, managing, and deploying a dashboard to gain insights about the built machine learning system

To get started, click on a card below, or see the previous table for a complete list of topics covered.

Query across distributed data sources as one: Data virtualization for data analytics


Learn about:

  • What is data virtualization?
  • Creating connections from databases hosted on multiple environments
  • Creating views from Joins and publish data to your current project

Monitor your machine learning models using Watson OpenScale in IBM Cloud Pak for Data


Learn about:

  • Storing custom models using open source technology
  • Setting up model fairness and model quality monitors with Watson OpenScale
  • Creating a project and setup a Python notebook on IBM Cloud Pak for Data

Build dashboards in Cognos Analytics on IBM Cloud Pak for Data


Learn about:

  • Building an IBM Cognos Analytics dashboard on IBM Cloud Pak for Data
  • Launching IBM Cognos Analytics on IBM Cloud Pak for Data
  • Importing and exporting dashboard binaries in the IBM Cloud Pak for Data environment

Predict, manage, and monitor the call drops of cell towers using IBM Cloud Pak for Data


Learn about:

  • Using Jupyter Notebooks to analyze data
  • Running Notebooks in IBM Cloud Pak for Data
  • Building, testing and deploying a machine learning model
  • Deploying a selected machine learning model
  • Creating a front-end app to interface with the deployed model


Next: Query across distributed data sources as one: Data virtualization for data analytics

Smruthi Raj Mohan
Srikanth Manne
Manjula G Hosurmath