TFX and Kubeflow pipeline concepts

Pipelines turn ML steps into a directed workflow: validate data → transform → train → evaluate → push. TensorFlow Extended (TFX) is Google’s production ML pipeline platform for scalable components. Kubeflow Pipelines (KFP) runs ML workflows on Kubernetes as graphs of containerized steps with caching, retries, and artifact tracking.

<!-- IMAGE: DAG of pipeline components with artifacts between nodes -->

Notebooks hide dependency order. Pipelines make order, inputs/outputs, and retries explicit—required for continuous training (MLOps level 1+) and for debugging “which step failed?”

1. Break work into components (one job each).
2. Wire them into a DAG (what must finish before what).
3. Pass parameters (small values) and artifacts (datasets, models, metrics).
4. Run on an orchestrator (KFP on Kubernetes; TFX on various runners).
5. Cache successful steps; retry flaky ones; record metadata.

A factory line: stations, conveyor handoffs, and a supervisor who restarts one station without rebuilding the whole plant.

Draw a 5-node DAG for one of your projects (ingest → validate → train → eval → register). Mark which edges pass files vs small configs.

⚠️ Myth: Pipelines are only for TensorFlow users.

✓ Reality: TFX is TF-centric; KFP components can wrap any containerized code.

⚠️ Myth: A pipeline removes the need for tests.

✓ Reality: Components still need unit/integration tests (level 2 CI).

• TFX: https://www.tensorflow.org/tfx ↗
• Kubeflow Pipelines docs: https://www.kubeflow.org/docs/components/pipelines/ ↗
• KFP pipeline concepts: https://www.kubeflow.org/docs/components/pipelines/concepts/pipeline/ ↗
• Google Cloud MLOps: https://docs.cloud.google.com/architecture/mlops-continuous-delivery-and-automation-pipelines-in-machine-learning ↗