# Ml Pipeline

Designs and implements production-grade ML pipeline infrastructure: configures...

- Repository: https://github.com/Jeffallan/claude-skills (12164 stars)
- Category: Design
- License: MIT
- Last updated: 2026-10-30
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add Jeffallan/claude-skills --skill ml-pipeline -g -a claude-code -y`
Codex: `npx skills add Jeffallan/claude-skills --skill ml-pipeline -g -a codex -y`
Cursor: `npx skills add Jeffallan/claude-skills --skill ml-pipeline -g -a cursor -y`
Gemini CLI: `npx skills add Jeffallan/claude-skills --skill ml-pipeline -g -a gemini-cli -y`
Claude Code plugin marketplace: `/plugin marketplace add Jeffallan/claude-skills`

## Description

Designs and implements production-grade ML pipeline infrastructure: configures experiment tracking with MLflow or Weights & Biases, creates Kubeflow or Airflow DAGs for training orchestration, builds feature store schemas with Feast, deploys model registries, and automates retraining and validation workflows. Use when building ML pipelines, orchestrating training workflows, automating model lifecycle, implementing feature stores, managing experiment tracking systems, setting up DVC for data versioning, tuning hyperparameters, or configuring MLOps tooling like Kubeflow, Airflow, MLflow, or Prefect.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# ML Pipeline Expert

Senior ML pipeline engineer specializing in production-grade machine learning infrastructure, orchestration systems, and automated training workflows.

## Core Workflow

1. **Design pipeline architecture**, Map data flow, identify stages, define interfaces between components
2. **Validate data schema**, Run schema checks and distribution validation before any training begins; halt and report on failures
3. **Implement feature engineering**, Build transformation pipelines, feature stores, and validation checks
4. **Orchestrate training**, Configure distributed training, hyperparameter tuning, and resource allocation
5. **Track experiments**, Log metrics, parameters, and artifacts; enable comparison and reproducibility
6. **Validate and deploy**, Run model evaluation gates; implement A/B testing or shadow deployment before promotion

## Reference Guide

Load detailed guidance based on context:

| Topic | Reference | Load When |
|-------|-----------|-----------|
| Feature Engineering | `references/feature-engineering.md` | Feature pipelines, transformations, feature stores, Feast, data validation |
| Training Pipelines | `references/training-pipelines.md` | Training orchestration, distributed training, hyperparameter tuning, resource management |
| Experiment Tracking | `references/experiment-tracking.md` | MLflow, Weights & Biases, experiment logging, model registry |
| Pipeline Orchestration | `references/pipeline-orchestration.md` | Kubeflow Pipelines, Airflow, Prefect, DAG design, workflow automation |
| Model Validation | `references/model-validation.md` | Evaluation strategies, validation workflows, A/B testing, shadow deployment |

## Code Templates

### MLflow Experiment Logging (minimal reproducible example)

```python
import mlflow
import mlflow.sklearn
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score, f1_score
import numpy as np

The rest: https://github.com/Jeffallan/claude-skills/blob/HEAD/skills/ml-pipeline/SKILL.md