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ml-pipeline

Automate design, orchestration, and deployment of machine learning pipelines.

2|Updated Jun 29, 2026
Search Tags:#automation#mlops#orchestration#pipeline#ml
One-click install
npx skills add https://github.com/Axel-DaMage/opencode-config --skill ml-pipeline-axel-damage
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: ml-pipeline
Source: https://github.com/Axel-DaMage/opencode-config/tree/main/skills/ml-pipeline
Command: npx skills add https://github.com/Axel-DaMage/opencode-config --skill ml-pipeline-axel-damage

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires mlflow, kubeflow, airflow, feast, scikit-learn, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill provides a comprehensive solution for designing, orchestrating, and automating end-to-end machine learning pipelines, enabling you to efficiently build and deploy production-grade ML solutions.

Core Features & Use Cases

  • ML Pipeline Design: Configure experiment tracking, feature stores, model registries, and automated retraining workflows.
  • Orchestration: Set up Kubeflow or Airflow DAGs for training orchestration, managing experiment tracking, and feature stores.
  • Automation: Implement feature engineering, distributed training, hyperparameter tuning, and MLOps tooling integration.
  • Use Case: Suppose you're working on a machine learning project and need to build a pipeline for training, validating, and deploying a model. Use this Skill to set up experiment tracking with MLflow, create a feature store with Feast, and deploy your model using a Kubeflow pipeline.

Quick Start

Run the ml-pipeline skill with the following command: ml-pipeline --name my-pipeline --description "A pipeline for training and deploying my model" --features "feature-engineering,training,validation,deployment".

Frequently Asked Questions about ml-pipeline

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

FAQPage Schema
How do I build and orchestrate end-to-end ML pipelines with Airflow and Kubeflow?▼

To build and orchestrate ML pipelines, you can configure Airflow or Kubeflow DAGs to automate training workflows, manage experiment tracking with MLflow, and handle feature stores using Feast for production-grade deployment.

What is the best way to automate ML pipeline deployment and experiment tracking?▼

Automating ML pipeline deployment involves using MLflow for experiment tracking, Feast for feature store management, and Kubeflow or Airflow DAGs to orchestrate training, validation, and deployment workflows end-to-end.

Do I need MLflow and Kubeflow configured before setting up automated retraining workflows?▼

Yes, setting up automated retraining workflows requires MLflow, Kubeflow, Airflow, Feast, and scikit-learn to be configured in your environment to properly manage experiment tracking, orchestration, and feature stores.

Can I use this ML pipeline orchestration approach for feature engineering and hyperparameter tuning?▼

Yes, ML pipeline orchestration supports implementing feature engineering, distributed training, hyperparameter tuning, and MLOps tooling integration to automate complex machine learning workflows from data preparation to deployment.

How do I set up a feature store with Feast for an ML training pipeline?▼

Setting up a feature store with Feast for an ML training pipeline involves configuring it alongside MLflow for experiment tracking and Kubeflow or Airflow DAGs to orchestrate the end-to-end training and deployment workflow.

When should I not use Kubeflow or Airflow DAGs for ML pipeline orchestration?▼

You should avoid using Kubeflow or Airflow DAGs for ML pipeline orchestration if your project lacks the prerequisite dependencies like MLflow and Feast, or if your machine learning workflow does not require complex automated retraining and distributed training.

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