AutoML Runner
The AutoML Runner automates the workflow of creating, training, and deploying machine learning models. It integrates seamlessly with the Data Science Lab (DSLab) module and allows you to import and operationalize models directly in a pipeline.
The AutoML Runner processes input data from one event, applies the trained AutoML model, and outputs enriched data (for example, with prediction columns) to another event.
Requirements
Before using the AutoML Runner, ensure that:
A model has been created and saved in the DSLab module.
Input and output events are available in the pipeline workflow.
Sufficient compute resources (CPU/GPU) are configured for model execution.
Using the AutoML Runner in a Pipeline Workflow
Drag and drop the AutoML Runner component into the Workflow Editor.
Create two events (input and output) and add them to the workspace.
Input data may originate from ingestion components, readers, DSLab scripts, or shared events.
Connect the input event → AutoML Runner → output event.
Select the AutoML Runner component to access its configuration tabs.
Configure the fields in the Basic Information and Meta Information tabs.
Save the component.
Once configured, the AutoML Runner consumes data from the input event, executes the trained model, and outputs the processed data with predictions to the output event.
Configuration
The AutoML Runner configuration is grouped into three sections:
Basic Information
Meta Information
Resource Configuration
Basic Information Tab
The Basic Information tab defines general properties and execution behavior.
Invocation Type
Select execution mode: Batch or Real-Time.
Yes
Grace Period (sec)
Appears only for Batch mode. Time before the component shuts down gracefully.
Conditional
Deployment Type
Displays the deployment type of the component (pre-selected).
Yes
Container Image Version
Displays the Docker image version used (pre-selected).
Yes
Failover Event
Select an event to handle failover scenarios.
Optional
Batch Size
Maximum records processed per cycle (minimum 10).
Yes
Meta Information Tab
The Meta Information tab links the AutoML Runner to a project and model in the DS Lab module.
Project Name
Name of the project containing the AutoML model.
Yes
Model Name
Name of the saved model within the project.
Yes
Saving the AutoML Runner
Click the Save Component (Storage icon).
A success message confirms that the component has been saved.
Example Workflow
Ingest product sales data through a Reader component.
Pass the data to the AutoML Runner, configured with a demand forecasting model.
Write the enriched dataset (with predicted demand values) to an output Event.