Ml Pipeline
Designs and implements production-grade ML pipeline infrastructure: configures experiment tracking with MLflow or Weights & Biases, creates Kubeflow or Airflow DAGs for training orchestration, buil...
About this skill
name: ml-pipeline description: Use when Designs and implements production-grade ML pipeline infrastructure: configures experiment tracking with MLflow or Weights & Biases, creates Kubeflow or Airflow DAGs for training orchestration, buil...
Ml Pipeline
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. — One of 66 full-stack developer skills in Jeff Allan's claude-skills plugin (MIT, 9.4k stars).
What you get
- Public GitHub repo (Jeffallan/claude-skills)
- the skills/ml-pipeline folder with SKILL.md and references. Install via /plugin install fullstack-dev-skills@jeffallan.
Customize your output
- Fork the repo and extend the skill's reference files for your own stack conventions.
Example output
Activates on a matching request (e.g. building or reviewing Ml Pipeline code) and can chain with other skills in the pack.
Best for
Full-stack developers and engineering teams using Claude Code.
SKILL.md preview
---
name: ml-pipeline
description: Use when Designs and implements production-grade ML pipeline infrastructure: configures experiment tracking with MLflow or Weights & Biases, creates Kubeflow or Airflow DAGs for training orchestration, buil...
---
# Ml Pipeline
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