In this code pattern we demonstrate a way to monitor your AI models in an application using Watson OpenScale in IBM Cloud Pak for Data. This will be demonstrated with an example of a Telecomm Call Drop Prediction Model.
In this code pattern we demonstrate how to create a model to predict call drops. With the help of an interactive dashboard, we use a time series model to better understand call drops. As a benefit to telecom providers and their customers, it can be used to identify issues at an earlier stage, allowing more time to take the necessary measures to mitigate problems.
The interconnectedness of today’s world poses unique challenges that force corporate security and intelligence units to adopt new risk management tools. Rather than...
In this Code Pattern, we will use German Credit data to train, create, and deploy a machine learning model using IBM Watson Machine Learning on IBM Cloud Pak for Data. We will create a data mart for this model with Watson OpenScale and configure OpenScale to monitor that deployment, then inject seven days' worth of historical records and measurements for viewing in the OpenScale Insights dashboard.
Decisions based on machine learning (ML) are potentially advantageous over human decisions, as they do not suffer from the same subjectivity, and can...
