This code pattern is part of a series that explores telecom call-drop predictions using IBM Cloud Pak® for Data, data virtualization, IBM Watson® OpenScale, and Cognos® Analytics.
|201||Query across distributed data sources as one: Data virtualization for data analytics||Tutorial|
|201||Monitor your machine learning models using Watson OpenScale in IBM Cloud Pak for Data||Pattern|
|301||Build dashboards in Cognos Analytics on IBM Cloud Pak for Data||Tutorial|
|301||Predict, manage, and monitor the call drops of cell towers using IBM Cloud Pak for Data||Pattern|
This code pattern will show you how to create a model to predict call drops. With the help of an interactive dashboard, we’ll use a time series model to better understand call drops.
In this code pattern, you’ll learn how to:
- Use data virtualization
- Create connections from databases hosted on multiple cloud (AWS, Azure, or IBM Cloud) or on-premises environments
- Create views from joins and publish data to your current project
- Store custom models using open source technology on Watson Machine Learning
- Deploy a model and connect the model deployment to Watson OpenScale on IBM Cloud Pak for Data and IBM Cloud
- Set up model fairness and model quality monitors in Watson OpenScale on IBM Cloud Pak for Data and on IBM Cloud using a Python notebook
- Create a project and set up a Python notebook on IBM Cloud Pak for Data
- AI models virtualize and join data stored across various sources, like AWS Cloud and IBM Cloud, as needed.
- Joined data is stored in the internal database of IBM Cloud Pak for Data and assigned to the current working project.
- Create machine learning models using Jupyter Notebooks to predict call drops per tower and a time-series model that projects a call-drop percentage based on real-time conditions.
- Model trained and stored in Watson Machine Learning, which is also connected to Watson OpenScale.
- Visualize and analyze insights from the trained models and the data using Cognos Analytics dashboards.
- Configure fairness, quality, and explainability monitors for each tower’s model.
Find the detailed steps for this pattern in the README. The steps show you how to:
- Clone the repository.
- Obtain your data from data virtualization.
- Create a new project in IBM Cloud Pak for Data.
- Upload the data set to IBM Cloud Pak for Data.
- Import the notebook to IBM Cloud Pak for Data.
- Follow the steps in the notebook.
- Set up your notebook for call drop monitoring.
- Set up the Cognos Analytics Dashboard on your IBM Cloud Pak for Data instance for visualizations.