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.
Collected from public GitHub. Every version is saved exactly as it was and never changes. Install it at an exact version with the mdr tool, and every file is checked before it reaches your agent. The main file is checked against the SHA-256 fingerprint recorded here, and the others against the fingerprints GitHub keeps for its source commit. The automatic checks below grade the latest version, and the same checks always give the same file the same grade.
What the file says
# ML Pipeline Workflow
Complete end-to-end MLOps pipeline orchestration from data preparation through model deployment.
## Overview
This skill provides comprehensive guidance for building production ML pipelines that handle the full lifecycle: data ingestion → preparation → training → validation → deployment → monitoring.
## When to Use This Skill
- 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
### Core Capabilities
1. **Pipeline Architecture**
- End-to-end workflow design
- DAG orchestration patterns (Airflow, Dagster, Kubeflow)
- Component dependencies and data flow
- Error handling and retry strategies
2. **Data Preparation**
- Data validation and quality checks
- Feature engineering pipelines
- Data versioning and lineage
- Train/validation/test splitting strategies
3. **Model Training**
- Training job orchestration
- Hyperparameter management
- Experiment tracking integration
…
The line at the top of this page pins the label git:20251120.50bf83d, which is what most people want. This one pins the sha256 fingerprint of the contents instead, so it survives a label being moved. Either way the exact version is written to mdr.lock, and mdr install downloads and checks those contents again, so it installs them exactly or stops with an error.
GET https://markdownregistry.com/api/v1/artifacts/art_tajuuiojulnalhde
GET https://markdownregistry.com/api/v1/resolve?ref=hermeticormus/claude-code-game-development/ml-pipeline-workflow
GET https://markdownregistry.com/api/v1/blob/79d76e60742409836da137d9d3b17a956a87aea2470f7fcead5829eb91469bc8
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