Module 7 - ML Pipeline Orchestration
ML pipelines are not a single script. They are chains of dependent steps - data ingestion, validation, training, evaluation, packaging, deployment - that must run reliably, in order, with retries, monitoring, and full audit trails. Orchestration is how you make that happen.
This module teaches you to design, build, and operate ML pipelines across the major orchestration platforms. You will understand the underlying primitives before touching any framework, and you will know how to choose the right tool for your team's context.
What You Will Learn
Lessons in This Module
| # | Lesson | What You Learn |
|---|---|---|
| 01 | Pipeline Orchestration Concepts | DAGs, idempotency, dependency management, why cron fails |
| 02 | Apache Airflow for ML | DAG authoring, XCom, executors, production Airflow |
| 03 | Prefect for ML | Flows, tasks, deployments, Prefect vs Airflow |
| 04 | Kubeflow Pipelines | KFP SDK, component authoring, Kubernetes-native ML |
| 05 | ZenML and Modern Orchestrators | ZenML stacks, Metaflow, orchestrator comparison matrix |
| 06 | Pipeline Testing and Reliability | Contract testing, chaos engineering, SLAs, runbooks |
| 07 | Scheduling and Triggering | Cron, event-driven, backfill, dynamic scheduling |
Key Concepts at a Glance
DAG (Directed Acyclic Graph): The fundamental data structure behind all ML orchestrators. Nodes are tasks; edges are dependencies. Acyclic means no circular dependencies - pipelines always terminate.
Idempotency: Running a pipeline step twice produces the same result. Critical for safe retries.
Executor: The component that actually runs tasks - locally, on Celery workers, or on Kubernetes pods.
XCom: Airflow's mechanism for passing small data between tasks. For large artifacts (models, datasets), always use external storage.
Prefect Flow: The top-level unit in Prefect - a Python function decorated with @flow. Tasks inside it are @task-decorated functions.
KFP Component: A self-contained, containerized unit of work in Kubeflow Pipelines. Takes typed inputs, produces typed outputs, registers artifacts.
ZenML Stack: A collection of infrastructure components (artifact store, orchestrator, experiment tracker) that defines where and how a pipeline runs.
Why This Module Matters
Every ML system beyond a single notebook needs orchestration. Without it:
- Steps run in the wrong order or not at all
- One failure corrupts downstream results silently
- Reruns are manual and error-prone
- There is no audit trail of what ran when with what data
- Scheduling relies on cron jobs that nobody monitors
After this module, you will be able to design an orchestration strategy from scratch, implement it in the tool your team uses, and build pipelines that fail loudly, recover gracefully, and are fully observable.
:::tip Module Prerequisite You should be comfortable with Python and have a basic understanding of what an ML training pipeline looks like (data → preprocessing → training → evaluation). No prior orchestration experience required. :::