{"id":155,"date":"2020-09-01T08:02:32","date_gmt":"2020-09-01T08:02:32","guid":{"rendered":"https:\/\/machine-learning.webcloning.com\/2020\/09\/01\/predict-manage-and-monitor-the-call-drops-of-cell-towers-using-ibm-cloud-pak-for-data\/"},"modified":"2020-09-01T08:02:32","modified_gmt":"2020-09-01T08:02:32","slug":"predict-manage-and-monitor-the-call-drops-of-cell-towers-using-ibm-cloud-pak-for-data","status":"publish","type":"post","link":"https:\/\/salarydistribution.com\/machine-learning\/2020\/09\/01\/predict-manage-and-monitor-the-call-drops-of-cell-towers-using-ibm-cloud-pak-for-data\/","title":{"rendered":"Predict, manage, and monitor the call drops of cell towers using IBM Cloud Pak for Data"},"content":{"rendered":"<div id=\"\">\n<!-- begin main body content --><\/p>\n<h2 id=\"summary\">Summary<\/h2>\n<p>This code pattern is part of a series that explores telecom call-drop predictions using IBM Cloud Pak for Data, data virtualization, Watson OpenScale, and Cognos Analytics.<\/p>\n<h2 id=\"description\">Description<\/h2>\n<p>This code pattern will show you how to create a model to predict call drops. With the help of an interactive dashboard, we\u2019ll use a time series model to better understand call drops.<\/p>\n<p>After completing this code pattern, you\u2019ll learn how to:<\/p>\n<ul>\n<li>Use data virtualization<\/li>\n<li>Create connections from databases hosted on multiple cloud (AWS, Azure, or IBM Cloud) or on-premise environments<\/li>\n<li>Create views from joins and publish data to your current project<\/li>\n<li>Store custom models using open source technology on Watson Machine Learning<\/li>\n<li>Deploy a model and connect the model deployment to Watson OpenScale on IBM Cloud Pak for Data and IBM Cloud<\/li>\n<li>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<\/li>\n<li>Create a project and set up a Python notebook on IBM Cloud Pak for Data<\/li>\n<\/ul>\n<h2 id=\"flow\">Flow<\/h2>\n<p><img class=\"lazycontent\" data-src=\"https:\/\/developer.ibm.com\/developer\/default\/patterns\/predict-manage-and-monitor-the-call-drops-of-cell-tower-using-cloud-pack-for-data\/images\/architecture2.png\" alt=\"Predict and manage calls flow diagram\"><\/p>\n<ol>\n<li>Data stored across various sources, like AWS Cloud and IBM Cloud, are virtualized and joined as needed by the AI models.<\/li>\n<li>The joined data is stored in the internal database of IBM Cloud Pak for Data and assigned to the current working project.<\/li>\n<li>Create machine learning models using Jupyter notebooks to predict call drops per tower and also a time-series model that projects a call drop percentage based on real-time conditions.<\/li>\n<li>Model trained and stored in Watson Machine Learning, which is also connected to Watson OpenScale.<\/li>\n<li>Visualize and analyze insights from the trained models and the data using Cognos Analytics dashboards.<\/li>\n<li>Configure fairness, quality, and explainability monitors for each tower\u2019s model.<\/li>\n<\/ol>\n<h2 id=\"instructions\">Instructions<\/h2>\n<p>Find the detailed steps for this pattern in the <a href=\"https:\/\/github.com\/IBM\/icp4d-telco-manage-ml-project\/blob\/master\/README.md\">README<\/a>. The steps show you how to:<\/p>\n<ol>\n<li>Clone the repository.<\/li>\n<li>Obtain your data from data virtualization.<\/li>\n<li>Create a new project in IBM Cloud Pak for Data.<\/li>\n<li>Upload the data set to IBM Cloud Pak for Data.<\/li>\n<li>Import the notebook to IBM Cloud Pak for Data.<\/li>\n<li>Follow the steps in the notebook.<\/li>\n<li>Set up your notebook for call drop monitoring.<\/li>\n<li>Set up the Cognos Analytics Dashboard on your IBM Cloud Pak for Data instance for visualizations.<\/li>\n<\/ol>\n<\/div>\n","protected":false},"excerpt":{"rendered":"<p>https:\/\/developer.ibm.com\/patterns\/predict-manage-and-monitor-the-call-drops-of-cell-tower-using-cloud-pack-for-data\/<\/p>\n","protected":false},"author":0,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":[],"categories":[3],"tags":[],"_links":{"self":[{"href":"https:\/\/salarydistribution.com\/machine-learning\/wp-json\/wp\/v2\/posts\/155"}],"collection":[{"href":"https:\/\/salarydistribution.com\/machine-learning\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/salarydistribution.com\/machine-learning\/wp-json\/wp\/v2\/types\/post"}],"replies":[{"embeddable":true,"href":"https:\/\/salarydistribution.com\/machine-learning\/wp-json\/wp\/v2\/comments?post=155"}],"version-history":[{"count":0,"href":"https:\/\/salarydistribution.com\/machine-learning\/wp-json\/wp\/v2\/posts\/155\/revisions"}],"wp:attachment":[{"href":"https:\/\/salarydistribution.com\/machine-learning\/wp-json\/wp\/v2\/media?parent=155"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/salarydistribution.com\/machine-learning\/wp-json\/wp\/v2\/categories?post=155"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/salarydistribution.com\/machine-learning\/wp-json\/wp\/v2\/tags?post=155"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}