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stephanj
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ml-pipeline

Design and implement scalable machine learning infrastructure with MLOps workflows.

9|2|Updated Jan 29, 2026
Search Tags:#mlops#orchestration#pipeline#mlflow#kubeflow#feature store
One-click install
npx skills add https://github.com/stephanj/claude-code-collections --skill ml-pipeline-stephanj
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: ml-pipeline
Source: https://github.com/stephanj/claude-code-collections/tree/main/skills/ml-pipeline
Command: npx skills add https://github.com/stephanj/claude-code-collections --skill ml-pipeline-stephanj

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pandas, scikit-learn, mlflow, wandb, feast, great_expectations, kfp, apache-airflow, prefect, and includes references (resource) components.

What problem does it solve?

This skill addresses the complexity of managing end-to-end machine learning lifecycles, ensuring that feature engineering, training, and deployment are reproducible, scalable, and observable.

Core Features & Use Cases

  • Pipeline Orchestration: Automates complex DAGs using Kubeflow, Airflow, or Prefect to manage dependencies between data and model tasks.
  • Experiment Tracking: Integrates with MLflow and Weights & Biases to log parameters, metrics, and artifacts for full reproducibility.
  • Feature Engineering: Provides patterns for building consistent feature stores and transformation pipelines that prevent training-serving skew.

Quick Start

Use the ml-pipeline skill to design a training workflow that includes feature engineering, experiment tracking, and model validation for a new classification project.

Frequently Asked Questions about ml-pipeline

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

FAQPage Schema
How do I orchestrate complex ML workflows with dependencies between data and model tasks?▼

To orchestrate complex ML workflows, this Skill automates DAGs using frameworks like Kubeflow, Airflow, or Prefect to manage dependencies between data ingestion, training, and deployment tasks. This ensures scalable and reproducible pipeline execution.

What is the best way to prevent training-serving skew in feature engineering pipelines?▼

Preventing training-serving skew requires consistent feature transformations. This Skill provides patterns for building consistent feature stores and transformation pipelines using Feast, ensuring feature definitions remain identical across training and serving environments.

How do I track ML experiments and ensure reproducibility across different runs?▼

Tracking ML experiments for reproducibility is achieved by integrating with MLflow and Weights & Biases. This Skill logs parameters, metrics, and artifacts automatically, providing full lineage and observability for every training run.

Can I use this pipeline orchestration for automated model lifecycle management and monitoring?▼

Yes, you can use this for automated model lifecycle management. It supports end-to-end MLOps workflows from data ingestion to production deployment and monitoring, satisfying requirements for data lineage and automated validation.

Does this MLOps workflow support automated data validation before training?▼

Automated data validation is supported within the MLOps workflow. By integrating Great Expectations, the pipeline validates data ingestion and transformation steps automatically, ensuring data quality before model training begins.

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