PMLE - Scaling Prototypes Into ML Models - Section 3.2

Train models by organising structured and unstructured data on Cloud Storage and BigQuery, ingesting from various sources, using SDKs such as Agent Platform custom training, Kubeflow on GKE, AutoML, and Tabular Workflows, troubleshooting training failures, tuning hyperparameters, and fine-tuning foundation models.

Organise training data on Cloud Storage and BigQuery, then submit jobs using Agent Platform custom training, Kubeflow on GKE, AutoML, or Tabular Workflows depending on control and scale requirements. Apply hyperparameter tuning to improve model quality, troubleshoot common training failures, and fine-tune foundation models for task-specific adaptation.

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