ahmad-ubaidillah/ml-pipeline-workflow
aizen-gate/skills-reference/.agent/skills/ml-pipeline-workflow/SKILL.md
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.
development
Updated Mar 27, 2026
$ install --global
skillsauthnpx skillsauth add ahmad-ubaidillah/aizen-gate ml-pipeline-workflowInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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SKILL.md
- 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
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