Many machine learning pipelines can be initially designed as a single script. However, production systems rarely operate this way. A typical ML workflow consists of multiple, interconnected stages: data validation, preprocessing, model training, evaluation, and deployment. If the "data validation" step fails, you should not proceed to "model training". If "training" succeeds, you want to automatically trigger "evaluation". Managing this sequence, handling failures, and scheduling runs requires more than a simple script. Orchestration tools fulfill this function.
An orchestration tool acts as the conductor for your ML pipeline. It doesn't perform the tasks itself, like data processing or model training. Instead, it directs which task should run, when it should run, and what to do if a task succeeds or fails. It manages the entire workflow from start to finish.
Automated pipelines without an orchestrator are like a factory assembly line with no central control system. Each station might work, but the overall process is brittle and hard to manage. Orchestrators provide the control system that makes pipelines reliable and scalable.
Here are the main problems they solve:
Orchestration tools define workflows using a structure called a Directed Acyclic Graph, or DAG. This sounds complex, but the idea is straightforward. A DAG is simply a way to describe a set of tasks and the dependencies between them.
Process Data to Train Model.Let's visualize a simple ML pipeline as a DAG.
A Directed Acyclic Graph (DAG) representing a machine learning pipeline. Each box is a task, and the arrows define the order of execution.
In this diagram, the orchestrator understands that it must first complete Ingest Data, then Validate Data, and so on. It will not attempt to train the model until the data has been successfully processed. The dashed line from Evaluate Model to Deploy Model represents conditional logic: the model is only deployed if it passes the evaluation criteria.
Several tools are available for orchestrating pipelines. While they all use the DAG structure, they differ in their design and primary use cases.
Apache Airflow is a mature, open-source platform for orchestrating general-purpose workflows. It is not designed exclusively for machine learning but is widely used in the field due to its flexibility and power.
Kubeflow is a project dedicated to making ML workflows on Kubernetes simple, portable, and scalable. Kubeflow Pipelines is the component responsible for orchestration.
As you'll see in our hands-on practical, tools like GitHub Actions can also serve as lightweight orchestrators. While not a dedicated orchestration platform like Airflow or Kubeflow, it is an excellent tool for CI/CD and simple, event-driven pipelines.
git push or a pull request.Choosing a tool depends on your project's scale and your team's environment. For simple CI tasks, GitHub Actions is sufficient. For complex, scheduled retraining pipelines, a dedicated tool like Airflow or Kubeflow provides more features for scheduling, monitoring, and recovery.
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