# Ml Ops Pipelines

> Orchestrating the end-to-end Machine Learning lifecycle with robust, reproducible pipelines.

- Skill: `jcorpac/ml-ops-pipelines` (Agent Skill)
- Install (CLI): `npx skillmds@latest add jcorpac/ml-ops-pipelines`
- Raw SKILL.md: https://api.skillmd.com/api/skills/jcorpac/ml-ops-pipelines/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: jcorpac (https://skillmd.com/u/jcorpac)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/jcorpac/ml-ops-pipelines

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# ML Ops Pipelines

ML is not code; it's code + data. Pipelines ensure that your training and deployment are predictable.

## The Workflow
1.  **Ingestion**: Loading data from sources.
2.  **Validation**: Checking data quality (TFX, Great Expectations).
3.  **Transformation**: Feature engineering.
4.  **Training**: Model creation.
5.  **Deployment**: Pushing to the model registry or serving layer.

## Tools
- **ZenML**: A framework-agnostic MLOps pipeline tool.
- **Kubeflow**: Running ML workflows on Kubernetes.
- **GitHub Actions**: For simple, CI-based ML pipelines.

## Best Practices
- **Versioning**: Version your data AND your code.
- **Automated Retraining**: Trigger pipelines when data drift is detected.