DevOps & Infrastructure
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.
- Publisher / source
- Jeffallan/claude-skills (found on skills.sh, an awesome list, the CSV list)
- Upstream record
- Jeffallan/claude-skills
- Version
- Version 2
- Published
- Publication review
- Approved for publication · recorded passed scan 2026-11-02
- Content fingerprint
f32d20c5fbaade8e7e43fcf756dec9543dd17f38b184f5d100b50bf34dae06a6