Creating a Machine Learning Model with AutoML

In this guide, you will learn how to build a Machine Learning model quickly using ARPIA's AutoML feature.

Step 1: Access the AI Workshop#

1. From your Home page in the Orchestrator, navigate to the AI Workshop.

Step 2: Select a Project#

2. Choose the project where you want to create the Machine Learning model.

Step 3: Add a New Model#

3. Click on the + button to start creating a new model.

Note: Ensure that your data source and output storage structure are available and properly set up in the workshop.

Step 4: Configure Model Details#

4. In the new window: * Select Machine Learning for the Type field. * Choose DK AutoML Engine as the Development Environment. * Enter a Name and an optional description for your model.

Step 5: Configure Engine Settings#

5. Set the following in the Engine Settings: * Choose preconfigured models. * Define the percentage of data for testing. * Enable dataset randomization if needed. * In AutoML Engine Status, select Metrics. * Opt to retrain on each batch if required.

Note: ARPIA's AutoML supports both classification and regression models. Learn more in the blog: ¿Clasificación o Regresión?

Step 6: Select Dataset Source#

6. In Engine Dataset Source: * Choose the table for training the model. * Select the columns to train the model (Features Columns). * Specify the column to predict (Target Column).

Step 7: Save and Run Settings#

7. Click on SAVE SETTINGS, then click RUN to initiate the model creation process.

Step 8: Execute the Model#

8. After clicking RUN, a new window will open. Confirm the execution by clicking on the Start button on the screen that opened. Monitor the training progress and results in the status section.

Step 9: Review Metrics#

9. Once the training is complete, the Metrics section will display the evaluation metrics. Review the models and select the one with the best accuracy.

Tip: Accuracy measures how well the model performs. Values closer to 1 indicate better performance.

Step 10: Deploy the Model#

10. In the ENGINE SETTINGS section, update AutoML Engine Status to Deployed.

Step 11: Save and Execute Deployment#

11. Click SAVE SETTINGS and then RUN to deploy the model.

Step 12: Review Final Metrics#

12. After deployment, revisit the Metrics section to verify the model’s performance and readiness.

Note: When a model is selected, a checkmark appears, indicating that it is ready for prediction.

Step 13: Predict New Data#

13. In the PREDICT NEW DATA section:
    • Choose the table where the model will be applied.
    • Select the columns that contain each required data.
    • Select the target column to be predicted.
    • Select the primary key of the table where the model will be applied.
    • Click SAVE SETTINGS and RUN to store predictions in the selected table.

Note: ARPIA will automatically create "predicted", "predicted_dt", and "predicted_accuracy" columns if they do not exist in the table.

Conclusion#

You have now successfully created and deployed your first AutoML model in ARPIA. Use it to make predictions and enhance your data-driven decisions!