Download PDF Abstract: Recent applications of autonomous agents and robots, for example,
self-driving cars, scenario-based trainers, exploration robots, service...
(Submitted on 2 Apr 2020) Abstract: Existing question answering systems can only predict answers without explicit
reasoning processes, which hinder their...
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.
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.