Workflow Patterns for Airflow and Prefect

10 Common ML Workflow Templates

This interactive worksheet guides enterprise AI teams through defining and selecting from 10 common machine learning workflow templates. It supports evaluation and implementation using Airflow or Prefect orchestration platforms.

Enterprise AI teams increasingly depend on workflow orchestration platforms such as Apache Airflow and Prefect to manage complex ML pipelines. Defining clear workflow templates helps establish repeatable, scalable processes for data ingestion, model training, and deployment.

This worksheet presents 10 common ML workflow templates along with typical tasks aligned to Airflow or Prefect implementations. It allows platform engineering leads and senior practitioners to select and customize workflows suited to their environments before integration.

Inputs

Pipeline execution frequency

Select how often your ML workflows run.

Include data validation step in pipeline?

YesNo

Model training type

Select the training setup typical for your use case.

Model deployment target

Select the typical deployment environment.

Include monitoring and alerting post-deployment?

YesNo

Primary orchestration platform

Select the orchestration tool for your workflows.

Result

Recommended ML Workflow Template Identifier

pipeline-frequency + '-' + data-validation + '-' + model-training + '-' + deployment-target + '-' + monitoring-included + '-' + orchestration-platform

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Suggested ML workflow template

Note

This worksheet provides an initial recommendation for ML workflow templates aligned with orchestration platforms Apache Airflow and Prefect. Teams should adapt templates to their specific operational constraints, compliance requirements, and scalability needs.

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