Chapter 2. Create an AutoML optimization run

Procedure

1. In the OpenShift AI dashboard, click Develop and train > AutoML.
2. Select a project and click Create AutoML optimization run.
3. Enter a name and optionally a description for the optimization run and click Next.

4. Configure the data source as follows:

   1. From the S3 connection list, select an existing data connection or click Add new connection.
   2. Click Browse bucket and select your CSV data file.

5. Select the prediction task type and configure the prediction settings:

   • Binary classification: Your target column has two distinct categories.
   • Multiclass classification: Your target column has three or more distinct categories.
   • Regression: Your target column has continuous numerical values.
   • Time series forecasting: Your data is sequential and you want to predict future values.

   1. For binary classification, multiclass classification, or regression:

      • Select the Label column from your data set.

   2. For time series forecasting:

      • Select the Target column with the numerical values to forecast.
      • Select the Timestamp column with the date or time values.
      • Select the ID column that identifies each time series.
      • Optional: Select Known covariates for values known in advance across the forecast horizon.
      • Set the Prediction length for the number of time steps to forecast.

6. Optional: Adjust the Top models to consider value (default 3, range 1 to 10 for tabular, 1 to 7 for time series).

   Note

   Optimization runs cannot be edited after creation. To stop, archive, or delete the underlying pipeline run, see Managing pipeline runs.

7. Click Create run.

   AutoML begins training models. You can monitor the run status on the AutoML page.