10.4.1.2.2.2 Build via AutoML

The AutoMLx python package automatically creates, optimizes and explains machine learning pipelines and models. The AutoML pipeline provides a tuned ML pipeline that finds the best model for a given training dataset and a prediction task at hand. AutoML has a simple pipeline-level Python API that quickly jump-starts the data science process with an accurate tuned model.

The AutoML pipeline consists of five major stages of the ML pipeline: preprocessing, algorithm selection, adaptive sampling, feature selection, and model tuning.

These pieces are readily combined into a simple AutoML pipeline which automatically optimizes the whole pipeline with limited user input/interaction.

Note:

The user needs to manually install the Oracle-AutoMLX Python package.

Figure 10-53 Build via AutoML

Follow these steps to build via AutoML:

1. Enter the Model Code for the model.
   This field is mandatory.
2. Enter the Description for the model.
3. Click on the Python Runtime dropdown to select any Conda environment.
   You can select the environment only if it is created. Otherwise it will be a default environment.
4. Click the toggle button to configure the pipeline.

   Note:

   You can enter the required inputs for all the parameters. However, they are not mandatory. You can also click the Help icon on the top right of the drawer for more details on each one of them.
5. Click on the Build Model.
   An AutoML model will be created and displayed in the Model Objectives page.