ml-pipeline-workflow

Name: ml-pipeline-workflow
Availability: InStock
Author: boraperusic

Orchestrate end-to-end ML pipelines from data ingestion to deployment and monitoring.

Updated Apr 12, 2026

Search Tags:#mlops #orchestration #dag #ml-pipeline #data-validation #experiment-tracking

Ready to run

npx skills add https://github.com/BoraPerusic/agents --skill ml-pipeline-workflow-boraperusic

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: ml-pipeline-workflow
Source: https://github.com/BoraPerusic/agents/tree/main/skills/to%20try/ml-pipeline-workflow
Command: npx skills add https://github.com/BoraPerusic/agents --skill ml-pipeline-workflow-boraperusic

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

Orchestrates the end-to-end lifecycle of ML workflows, reducing manual integration and operational overhead from data ingestion to deployment and monitoring.

Core Features & Use Cases

End-to-end pipeline orchestration across data ingestion, preparation, training, validation, deployment, and monitoring.
DAG-based orchestration patterns (Airflow, Dagster, Kubeflow, Prefect) with clear dependency management.
Data validation, experiment tracking, versioning, and automated deployment pipelines.
Use Case: Production ML workflows that require reproducibility, auditing, and reliable rollout across environments.

Quick Start

Create a minimal end-to-end ML pipeline that ingests data, trains a model, and deploys it to production.

Frequently Asked Questions about ml-pipeline-workflow

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

FAQPage Schema

How do I orchestrate an end-to-end ML pipeline from data ingestion to deployment?▼

End-to-end ML pipeline orchestration automates the flow from data ingestion, preparation, training, validation, deployment, and monitoring using DAG-based patterns to ensure reproducibility and reduce manual integration overhead.

Does this ML pipeline workflow support orchestration with Airflow, Dagster, and Kubeflow?▼

Yes, the ML pipeline workflow supports DAG-based orchestration patterns with Airflow, Dagster, Kubeflow, and Prefect, providing clear dependency management for production-grade data science workflows.

What's the best way to ensure reproducibility in production ML workflows?▼

Ensuring reproducibility in production ML workflows requires orchestrating pipelines with data validation, experiment tracking, versioning, and automated deployment to maintain reliable rollouts across environments.

Can I include data validation and experiment tracking in my ML pipeline?▼

Yes, you can include data validation and experiment tracking within the ML pipeline orchestration, enabling auditing and reliable model validation before automated deployment.

When do I need DAG-based orchestration for my machine learning pipelines?▼

You need DAG-based orchestration for machine learning pipelines when building production-grade workflows that require clear dependency management, reproducibility, auditing, and reliable rollouts across different environments.

Do I need an orchestrator to automate model training and deployment pipelines?▼

Yes, automating model training and deployment pipelines requires integration with orchestrators like Airflow, Dagster, Kubeflow, or Prefect to manage dependencies and enable reliable end-to-end execution.