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Chapter 1. AutoML overview

AutoML is an automated machine learning system in Red Hat OpenShift AI that finds the best model for your prediction task. You provide training data in CSV format and select a task type. AutoML trains and evaluates multiple models, ranks them on a leaderboard, and produces notebooks that you can use to run predictions with the best-performing model.

1.1. AutoML workflow
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When you create an AutoML optimization run, AutoML loads your training data, samples it if needed, splits it into training and test sets, and trains candidate models by using different algorithms and configurations. AutoML evaluates each model against the held-out test set. The leaderboard ranks models by the optimized metric for your task type, and you can sort by other metrics to compare performance. You can register a model to a model registry or save a notebook for evaluation and exploration. After you register a model, you can deploy it for inference with a compatible serving runtime.

1.2. Supported task types
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Binary classification
Predict outcomes with two distinct categories, such as pass or fail, or approved or denied.
Multiclass classification
Predict outcomes with three or more distinct categories, such as product categories or support ticket priorities.
Regression
Predict continuous numerical values, such as price, temperature, or duration.
Time series forecasting
Predict future values over a specified date or time range. Your data must include a timestamp column, a numeric target column, and an ID column that identifies each time series.

1.3. Technology Preview limitations
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The following limitations apply during Technology Preview:

  • CSV format training data only
  • Training data capped at 32 MiB when uploaded through the dashboard, or 100 MB when loaded from S3
  • No custom algorithm selection or hyperparameter tuning
  • Optimization runs cannot be edited after creation

1.4. Viewing externally created runs
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AutoML pipelines are automatically registered with your pipeline server when your pipeline server starts. If you create runs from these pipelines outside of the AutoML interface, the runs appear on the AutoML page in the dashboard.

To find the best model for your data, create an AutoML optimization run, evaluate the results, and register or test the best-performing model.

Before you begin, ensure that the following prerequisites are met:

  • A cluster administrator has set the value of the spec.dashboardConfig.automl dashboard configuration option to true. For more information, see Dashboard configuration options.
  • You have a pipeline server configured in your project. When configuring the pipeline server, select the Enable AutoML and AutoRAG pipelines checkbox in Advanced settings. If you create the DataSciencePipelinesApplication instance with YAML, set spec.apiServer.managedPipelines: {}. For more information, see Configuring a pipeline server.
  • Your training data is available in an S3-compatible storage bucket in CSV format.
Important

Upload updated AutoML pipeline definitions before you create your first run. For instructions and download links, see RHOAIENG-64768 - AutoML and AutoRAG pipeline runs fail with image pull errors in the release notes.

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