ml-pipeline-workflow

version	4.1.0-fractal
name	ml-pipeline-workflow
description	Build end-to-end MLOps pipelines from data preparation through model training, validation, and production deployment. Use when creating ML pipelines, implementing MLOps practices, or automating model training and deployment workflows.

ML Pipeline Workflow

Complete end-to-end MLOps pipeline orchestration from data preparation through model deployment.

Do not use this skill when

• The task is unrelated to ml pipeline workflow
• You need a different domain or tool outside this scope

Instructions

• Clarify goals, constraints, and required inputs.
• Apply relevant best practices and validate outcomes.
• Provide actionable steps and verification.
• If detailed examples are required, open resources/implementation-playbook.md.

Overview

This skill provides comprehensive guidance for building production ML pipelines that handle the full lifecycle: data ingestion → preparation → training → validation → deployment → monitoring.

Use this skill when

• Building new ML pipelines from scratch
• Designing workflow orchestration for ML systems
• Implementing data → model → deployment automation
• Setting up reproducible training workflows
• Creating DAG-based ML orchestration
• Integrating ML components into production systems

What This Skill Provides

🧠 Knowledge Modules (Fractal Skills)

1. Core Capabilities

2. Reference Documentation

3. Assets and Templates

4. Basic Pipeline Setup

5. Production Workflow

6. Pipeline Design

7. Data Management

8. Model Operations

9. Deployment Strategies

10. Orchestration Tools

11. Experiment Tracking

12. Deployment Platforms

13. Batch Training Pipeline

14. Real-time Feature Pipeline

15. Continuous Training

16. Common Issues

17. Debugging Steps