ml-pipeline — Skills — getagentictools

What it does

Version all data, code, and models explicitly (DVC, Git tags, model registry)

Pin dependencies and random seeds for reproducible training environments

Log all hyperparameters, metrics, and artifacts to experiment tracking

Validate data schema and distribution before training begins

Use containerized environments; store credentials in secrets managers, never in code

Implement error handling, retry logic, and pipeline alerting

Separate training and inference code clearly

ml pipeline

Source

Repository: https://github.com/jeffallan/claude-skills

ml-pipeline FAQ

What does the ml-pipeline skill do?

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. Version all data, code, and models explicitly (DVC, Git tags, model registry) Pin dependencies and random seeds for reproducible training environments

How do I install ml-pipeline?

Run: npx -y skills add https://github.com/jeffallan/claude-skills --skill ml-pipeline --agent claude-code — the source lives at github.com/jeffallan/claude-skills.

Maintain ml-pipeline?

Let people know it's listed here — add the badge (live metrics, light/dark aware) or a plain link to your README or docs.

[ml-pipeline on getagentictools](https://getagentictools.com/skills/jeffallan-claude-skills-ml-pipeline?ref=badge)

npx agentictools info skills/jeffallan-claude-skills-ml-pipeline

The second line is the CLI lookup for this page — handy in READMEs and docs.