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mlops-dag-builder

Design DAG-based MLOps pipelines for Airflow, Dagster, Kubeflow, or Prefect.

156|21|Updated Oct 25, 2025
Search Tags:#mlops#airflow#dag#prefect#kubeflow#dagster
One-click install
npx skills add https://github.com/anton-abyzov/specweave --skill mlops-dag-builder
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: mlops-dag-builder
Source: https://github.com/anton-abyzov/specweave/tree/main/plugins/specweave-ml/skills/mlops-dag-builder
Command: npx skills add https://github.com/anton-abyzov/specweave --skill mlops-dag-builder

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) and assets (resource) components.

What problem does it solve?

Guides building platform-agnostic, DAG-based MLOps pipelines (Airflow, Dagster, Kubeflow, Prefect) and lays out templates for end-to-end ML workflows.

Core Features & Use Cases

  • Architecture guidance for data preparation, training, validation, and deployment
  • Reference docs in references/ (data-preparation, model-training, model-validation, model-deployment)
  • Asset templates (pipeline-dag.yaml.template, training-config.yaml, validation-checklist.md)
  • Integration guidance for experiment tracking and CI/CD

Quick Start

Define a basic DAG with stages: data_ingestion → data_validation → model_training → model_deployment.

Frequently Asked Questions about mlops-dag-builder

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I design a DAG-based ML pipeline for Airflow, Dagster, Kubeflow, or Prefect?▼

DAG-based ML pipelines organize workflows into directed acyclic graphs with stages: data ingestion, validation, model training, and deployment. This Skill provides architecture guidance and reusable templates for building platform-agnostic pipelines across Airflow, Dagster, Kubeflow, and Prefect, covering data preparation through deployment automation.

Can I use the same pipeline template across multiple orchestration platforms?▼

Yes. This Skill delivers platform-agnostic templates and reference documentation for data preparation, model training, validation, and deployment that work across Airflow, Dagster, Kubeflow, and Prefect, eliminating the need to rewrite pipelines for each platform.

What should an MLOps pipeline include for production readiness?▼

Production MLOps pipelines require modularity, idempotency, observability, versioning, and failure handling. This Skill covers integration with experiment tracking, model registries, and cloud deployment platforms, plus templates for validation checklists and training configurations.

How do I integrate experiment tracking and CI/CD into my DAG pipeline?▼

This Skill provides integration guidance for connecting DAG pipelines to experiment tracking systems and CI/CD workflows, enabling reproducible model training, validation, and automated deployment across cloud and on-premises environments.

Does this approach work for both cloud and on-premises deployments?▼

Yes. DAG-based MLOps pipelines are applicable to both cloud and on-premises environments. This Skill includes deployment automation templates and guidance for multi-environment orchestration using Airflow, Dagster, Kubeflow, or Prefect.

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