Building an AutoAI model

AutoAI automatically prepares data, applies algorithms, and attempts to build model pipelines best suited for your data and use case. This topic describes how to generate the model pipelines.

Follow these steps to upload data and have AutoAI create the best model for your data and use case.

  1. Prepare your training data
  2. Create a project in Watson Studio
  3. Open the AutoAI tool
  4. Specify details of your model and training data and launch AutoAI
  5. View the results

Prepare your training data

Collect your model data in a CSV file that is less than 100MB. Where possible, AutoAI will transform the data and impute missing values.

Create a project in Watson Studio

For your convenience, your AutoAI model creation uses the default storage associated with your project to store your data and to save model results, so you do not have to set up any separate repositories.

  1. In Watson Studio, click the IBM Watson link in the header to navigate to the home panel.
  2. Click New project.
  3. If you are prompted to select a region, choose the US South region.
  4. If you don’t already have the required Watson Machine Learning service, follow the prompts to create new service instances.
  5. (Optional) Upload your data training file in CSV format as a data asset for the project.

Open the AutoAI tool

  1. In your Watson Studio project, click Add to project.

Note: After you create an AutoAI asset it will display on the Assets page for your project in the AutoAI experiment section, so you can return to it.

Specify details of your experiment

  1. Specify a name and description for your experiment.
  2. Choose data from your project or upload it from your file system, then press Continue. Data must be in a CSV file and must be smaller than 100MB.
  3. Choose the Feature column you want the experiment to predict.
    • Based on analyzing a subset of the data set, AutoAI chooses a default model type: binary classification, multiclass classification, or regression. Binary is selected if the target column has two possible values, multiclass if it has a discrete set of possible values, for example 5 or 10, and regression if there is an infinite number of possible values. You can override this selection.
    • AutoAI also chooses a default metric for optimizing. For example, the default metric for a binary classification model is ROC AUC, which balances precision, accuracy, and recall.
  4. (Optional) Click Edit prediction to change the model type or the metric to optimize.

Click Run Experiment to begin model pipeline creation.

A progress infographic shows you the creation of pipelines for your data. The duration of this phase depends on the size of your data set. A notification message informs you if the processing time will be brief or require more time. You can work in other parts of the product while the pipelines build.

Pipeline creation for AutoAI models

View the results

When the pipeline generation process completes, you can view the leading model candidates and evaluate them before saving a pipeline as a model.

Next step

Follow the steps in Selecting an AutoAI model for details on how to evaluate the pipelines as model candidates, then save a model.