skills.rest
leonardoteodoroo
leonardoteodorooCOMMUNITY
Explore all skills by this author →
GitHub

ml-pipeline-workflow

Automate end-to-end ML pipeline orchestration from data preparation to deployment.

Updated Feb 3, 2026
Search Tags:#mlops#orchestration#pipeline#ml#model-training#data-preparation
One-click install
npx skills add https://github.com/leonardoteodoroo/amino-advanced --skill ml-pipeline-workflow-leonardoteodoroo
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: ml-pipeline-workflow
Source: https://github.com/leonardoteodoroo/amino-advanced/tree/main/.agent/skills/ml-pipeline-workflow
Command: npx skills add https://github.com/leonardoteodoroo/amino-advanced --skill ml-pipeline-workflow-leonardoteodoroo

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

Build production-ready ML pipelines that orchestrate data ingestion, preparation, training, validation, deployment, and monitoring, reducing manual wiring and drift risk.

Core Features & Use Cases

  • End-to-end pipeline orchestration across data prep, model training, validation, and deployment
  • DAG-based orchestration patterns (Airflow, Dagster, Kubeflow) with clear data dependencies
  • Data quality, versioning, experiment tracking, and deployment automation for reproducible ML workflows
  • Use Case: Design a repeatable ML lifecycle for new models from raw data to live endpoints with monitoring

Quick Start

Create a basic end-to-end ML pipeline from raw data to deployment in your environment.

Frequently Asked Questions about ml-pipeline-workflow

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

FAQPage Schema
How do I build an end-to-end ML pipeline from data preparation to deployment?▼

End-to-end ML pipelines orchestrate data ingestion, preprocessing, training, validation, and deployment using DAG-based patterns. This automation reduces manual wiring and establishes clear data dependencies for reproducible model workflows.

What is DAG-based orchestration in MLOps workflows?▼

DAG-based orchestration structures ML pipeline tasks as a Directed Acyclic Graph to manage data dependencies across steps like data prep and model training. It enables reliable execution across tools like Airflow, Dagster, or Kubeflow.

Can I use Airflow, Dagster, or Kubeflow for ML pipeline orchestration?▼

Yes, ML pipeline orchestration supports DAG-based patterns across Airflow, Dagster, and Kubeflow. These frameworks manage data dependencies and automate the workflow from data preparation through to deployment and monitoring.

How do I automate data validation and experiment tracking in ML pipelines?▼

You automate data validation and experiment tracking by integrating them into your DAG-based ML pipeline. This ensures data quality and versioning are continuously checked alongside model training, maintaining reproducible ML workflows.

Does ML pipeline orchestration handle deployment automation and monitoring?▼

Yes, ML pipeline orchestration handles deployment automation and monitoring. It extends the workflow from model validation to live endpoints, applying observability patterns to detect drift and ensure production stability.

Related Skills

Explore More Data & Analytics Skills→
meleantoniomeleantonio
598

stata-data-cleaning

Clean and transform messy Stata datasets into analysis-ready data with do-files.

Community
Intermediate
fmindfmind
1.4k

MLOps Observability

Configure ML observability with MLflow, Evidently, and SHAP for drift detection and alerting.

Community
Advanced
pmcdowallpmcdowall

weights-and-biases

Track machine learning experiment metrics, configurations, and model artifacts with Weights & Biases.

Community
Advanced
GitnappGitnapp

modal-serverless-gpu

Deploy and manage machine learning workloads on Modal serverless GPU infrastructure.

Community
Advanced