Integration with Machine Learning
Machine Learning extracts key characteristics, patterns, and anomalies from your historical data to create predictive models. These models then contain insights from your data that you can transform into actions and business decisions. Your company's historical data contains information that can improve your business decisions.
Watson Machine Learning and
Machine Learning for z/OS® are transactional AI platforms. Each provides a web user interface, various APIs, and a web administration dashboard with a suite of tools for model development and deployment, user management and system administration. For more information, see the IBM® Machine Learning for IBM z/OS documentation
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By integrating Machine Learning predictive models into Operational Decision Manager in Decision Runtime for z/OS, you can apply predictive insights from historical data and prescriptive business decisions based on company policies.
Setup and usage
| Activity | Description | Information |
|---|---|---|
| Integrating | To call Machine Learning models from your rule project, you must import a machine learning model into Rule Designer. | Integrating Machine Learning |
| Configuring | You create an ml.properties file with the required Machine Learning service configuration and set it as a XOM resource. This approach offers the advantage of defining the endpoint for each RuleApp, enhancing customization and flexibility in managing Machine Learning configurations. It can be done by using Rule Designer, the Rule Execution Server console or REST API. | Adding a Machine Learning configuration file as a XOM resource |
| Running | If calling Machine Learning natively on z/OS, you must configure an external Liberty server with both Machine Learning for IBM z/OS and Operational Decision Manager before you can use the two products together. | Configuring the runtime to use Machine Learning with Operational Decision Manage |
| Example | An example that calls Machine Learning churn predictions that you can use in your rules. | Example of a Machine Learning call |
| Tutorial | Churn is an important metric for borrower retention and as such is critical in decision-making processes. In this tutorial, you enhance the Miniloan rule project with borrower churn predictions from Machine Learning. | Enhancing rules with Machine Learning predictions |