Train Scikit-learn Models on the Cloud

Machine learning teams may need cloud infrastructure as datasets, checkpoints, and logs grow, but managing cloud resources can add operational complexity. Using the Iris dataset, the walkthrough first demonstrates conventional Scikit-learn decision-tree training, evaluation, and visualization, then shows how to migrate the workflow into a LightningWork component. Lightning’s CloudCompute configuration allows users to select resources such as CPU and disk capacity, while its Drive API provides persistent shared storage for saved model checkpoints that can later support deployment or other workflow components. The application can run locally or in the cloud through Lightning commands, with training logs, code, artifacts, and downloadable models available through the Lightning AI dashboard. The approach can also support scheduled or condition-triggered training, deployment, and cloud-based hyperparameter optimization, while existing Scikit-learn scripts can generally be migrated by placing configuration in a LightningWork initializer and training logic in its run method.

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