Uploaded June 2026 | Updated September 2026, 2 weeks ago

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In this video, our engineering team takes you through a full end-to-end Kubeflow implementation, step by step – from data exploration to production inference.

Follow the journey of a house price prediction use case and see how modern MLOps components work together:

✅ Kubeflow architectures and starter repositories
✅ Notebook-based development workflows
✅ Data exploration and model development
✅ MLflow for experiment tracking
✅ Katib for hyperparameter optimization
✅ Kubeflow Pipelines for automated preprocessing and training
✅ KServe for scalable model inference

🎥 Watch a practical introduction to building reproducible, scalable, and production-ready ML platforms.

Learn more about Kubeflow or contact our team: canonical.com/mlops/kubeflow

See it in action, then try it yourself. Start your Kubeflow journey in minutes with Managed Kubeflow on Azure: marketplace.microsoft.com/en-us/product/saas/canonical.managed-kubeflow