Build end-to-end MLOps pipelines from data preparation through model training, validation, and production deployment. Use when creating ML pipelines, implementing MLOps practices, or automating model training and deployment workflows.
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ML Pipeline Workflow
Complete end-to-end MLOps pipeline orchestration from data preparation through model deployment.
Overview
This skill provides comprehensive guidance for building production ML pipelines that handle the full lifecycle: data ingestion → preparation → training → validation → deployment → monitoring.
When to Use This Skill
Building new ML pipelines from scratch
Designing workflow orchestration for ML systems
Implementing data → model → deployment automation
[Description truncada. Veja o README completo no GitHub.]
