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ML Pipelines

PAIS provides a full ML pipeline stack for automating training, evaluation, and deployment workflows.

Components

Component Purpose URL
Kubeflow Pipelines Define and run multi-step ML workflows as DAGs https://kubeflow.pais.auckland.ac.nz
Katib Automated hyperparameter tuning (Bayesian, random, grid search) Kubeflow → Experiments (AutoML)
MLflow Experiment tracking, model registry https://mlflow.pais.auckland.ac.nz
KServe Model serving (deploy a trained model as an inference endpoint) Kubeflow → Models

When to use pipelines vs notebooks

Scenario Use
Exploratory analysis, prototyping Notebook
Multi-hour training jobs Pipeline (runs unattended)
Hyperparameter sweeps Katib
Reproducible, versioned workflows Pipeline
Model serving post-training KServe