OAC

Applying a Machine Learning Model in OAC

Posted on Updated on

There are a number of different tools and languages available for machine learning projects. One such tool is Oracle Analytics Cloud (OAC).  Check out my article for Oracle Magazine that takes you through the steps of using OAC to create a Machine Learning workflow/dataflow.

Screenshot 2019-12-19 14.31.24

Oracle Analytics Cloud provides a single unified solution for analyzing data and delivering analytics solutions to businesses. Additionally, it provides functionality for processing data, allowing for data transformations, data cleaning, and data integration. Oracle Analytics Cloud also enables you to build a machine learning workflow, from loading, cleaning, and transforming data and creating a machine learning model to evaluating the model and applying it to new data—without the need to write a line of code. My Oracle Magazine article takes you through the various tasks for using Oracle Analytics Cloud to build a machine learning workflow.

That article covers the various steps with creating a machine learning model. This post will bring you through the steps of using that model to score/label new data.

In the Data Flows screen (accessed via Data->Data Flows) click on Create. We are going to create a new Data Flow to process the scoring/labeling of new data.

Screenshot 2019-12-19 15.08.39

Select Data Flow from the pop-up menu. The ‘Add Data Set’ window will open listing your available data sets. In my example, I’m going to use the same data set that I used in the Oracle Magazine article to build the model.  Click on the data set and then click on the Add button.

Screenshot 2019-12-19 15.14.44

The initial Data Flow will be created with the node for the Data Set. The screen will display all the attributes for the data set and from this you can select what attributes to include or remove. For example, if you want a subset of the attributes to be used as input to the machine learning model, you can select these attributes at this stage. These can be adjusted at a later stages, but the data flow will need to be re-run to pick up these changes.

Screenshot 2019-12-19 15.17.48

Next step is to create the Apply Model node. To add this to the data flow click on the small plus symbol to the right of the Data Node. This will pop open a window from which you will need to select the Apply Model.

Screenshot 2019-12-19 15.22.40

A pop-up window will appear listing the various machine learning models that exist in your OAC environment. Select the model you want to use and click the Ok button.

Screenshot 2019-12-19 15.24.42

Screenshot 2019-12-19 15.25.22

The next node to add to the data flow is to save the results/outputs from the Apply Model node. Click on the small plus icon to the right of the Apply Model node and select Save Results from the popup window.

Screenshot 2019-12-19 15.27.50.png

We now have a completed data flow. But before you finish edit the Save Data node to give a name for the Save Data Set, and you can edit what attributes/features you want in the result set.

Screenshot 2019-12-19 15.30.25.png

You can now save and run the Data Flow, and view the outputs from applying the machine learning model. The saved data set results can be viewed in the Data menu.

Screenshot 2019-12-19 15.35.11