With Bring Your Own ML, EPM administrators can import a fully trained Machine Learning (ML) model and deploy it to a Planning application. Planners can then leverage robust, ML-based forecasting that uses advanced predictive modeling techniques to generate more accurate forecasts.
Data scientists gather and prepare historical data related to a business problem, train the algorithm, and generate a PMML file (Predictive Model Markup Language, a standard language used to represent predictive models) using a third party tool. These predictive analytic models and machine learning models use statistical techniques or ML algorithms to learn patterns hidden in large volumes of historical data. Predictive analytic models use the knowledge acquired during training to predict the existence of known patterns in new data.
EPM administrators can then import and configure the fully trained ML model, which generates two Groovy rules. Adminstrators attach the rule to a form or dashboard, or schedule a job to generate prediction results on a regular basis. This puts the benefits of machine learning and the power of data science into the hands of business users, enhancing the planning and budgeting process and leading to better business decisions.
For example, you can predict product volume for an entity, using key drivers such as average sales price, planned spend on promotions and advertising, historical volumes, and estimated industry volumes.
You can import ML Models and use them to predict numeric values in other finance use cases, for example:
- Trade promotion impacts on sales uplifts
- Marketing mix modeling to drive better ROMI
- Internal and external driver impacts on revenue forecasts
- Predictive cash forecasting for better cash position
Overview of Steps
Prerequisite: Data scientists build and train the ML model in a data science tool (any third party tool or Oracle Data Science Cloud) and save it as a PMML file.
Next, EPM administrators put the model to work to get business value from the trained model:
Administrators import the ML model in PMML format to a Planning application and define how the input variables and target variable maps to dimension members or cell values in the Planning application. This step generates automatic Groovy rules that connect the ML model to the Planning application. Two Groovy rules are generated for each ML model definition: one rule to associate with a form or dashboard, which allows users to make predictions on demand, and another to generate large scale predictions in a scheduled job for bulk processing. See Importing an ML Model.
- Administrators deploy the ML model in a Planning application by associating the Groovy rule to relevant action menus, forms, or dashboards. See Deploying an ML Model to Planners. Administrators can also create a job to run the Groovy rule in a batch process.
- Planners leverage ML-powered business rules in forms to generate predicted values, which are saved on the form. Planners can perform what-if analysis using the generated predictions, or modify predicted values on the form. Planners add value with their expertise and judgement, and then finalize the forecast.
This is an iterative process. As planners make predictions based on the ML model, administrators can measure the performance of the model, and can work with data scientists to update or replace the ML model when needed. Then, administrators re-import and deploy the retrained ML model.
When you re-import the retrained ML model, the Groovy rules are regenerated.
|Your Goal||Watch This|
|This overview introduces you to Bring Your Own ML (Machine Learning), where EPM Administrators can import a fully trained ML model and deploy it to a Planning application. Planners can then leverage robust, ML-based forecasting that uses advanced predictive modeling techniques to generate more accurate forecasts.||Overview: Bring Your Own Machine Learning (ML)|
Learn how to configure ML model import for Bring Your Own Machine Learning. You import a fully trained ML model into Planning. You follow the steps in a wizard to map, analyze, and test the model. After saving the model, two Groovy rules are created. To complete the integration process, see the related video for deploying an ML model to Planning.
|Configuring Machine Learning (ML) Model Import|
Learn how to deploy an ML Model to Planning. After configuring the ML Model Import, you integrate the PMML file into your planning application by creating an Action Menu with the Groovy rule generated from the configured ML model. Then you associate the Action Menu with a Planning form. When Planners run the rule from the form, the rule returns the set of predicted values.