ml-pipeline-workflow

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

Build end-to-end MLOps pipelines for model training and deployment.

Updated Dec 23, 2025

Search Tags:#deployment #mlops #model training #orchestration #pipeline #data preparation

Ready to run

npx skills add https://github.com/drgaciw/academic-compliance-hub-glm --skill ml-pipeline-workflow-drgaciw

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: ml-pipeline-workflow
Source: https://github.com/drgaciw/academic-compliance-hub-glm/tree/main/agents/plugins/machine-learning-ops/skills/ml-pipeline-workflow
Command: npx skills add https://github.com/drgaciw/academic-compliance-hub-glm --skill ml-pipeline-workflow-drgaciw

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill streamlines the creation and management of complex Machine Learning Operations (MLOps) pipelines, automating the entire lifecycle from data preparation to model deployment and monitoring.

Core Features & Use Cases

End-to-End Orchestration: Manages data ingestion, preparation, training, validation, and deployment.
Reproducible Workflows: Ensures consistent and automated ML model development and deployment.
Use Case: Automate the retraining and deployment of a fraud detection model whenever new transaction data exceeds a certain volume or shows signs of drift.

Quick Start

Use the ml-pipeline-workflow skill to set up a new MLOps pipeline with data preparation, model training, and deployment stages.

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 MLOps pipeline for model training and deployment?▼

End-to-end MLOps pipelines orchestrate data ingestion, preparation, model training, validation, and production deployment. This Skill builds automated, reproducible workflows covering the complete ML lifecycle from data preparation to deployment.

What's the best way to automate retraining and deployment when new data arrives?▼

Automating retraining and deployment requires an orchestrated MLOps pipeline that triggers model training and validation based on data volume or drift. This Skill sets up event-driven workflows to manage the complete ML lifecycle automatically.

Does this MLOps pipeline workflow integrate with Airflow, Kubeflow, MLflow, and SageMaker?▼

Yes, this MLOps pipeline workflow integrates with orchestration tools like Airflow, Kubeflow, MLflow, and SageMaker. These integrations provide robust ML lifecycle management across data preparation, training, validation, and deployment stages.

How do I ensure reproducible workflows for machine learning model development?▼

Reproducible workflows for machine learning model development require consistent, automated pipeline orchestration. This Skill manages data preparation, training, validation, and deployment stages to ensure automated and reproducible ML model development.

Can I use this pipeline orchestration for fraud detection model deployment?▼

Yes, pipeline orchestration supports fraud detection model deployment by automating retraining when new transaction data exceeds a volume threshold or shows drift. It manages the entire workflow from data preparation through production deployment.