ml-pipeline-workflow

# ML Pipeline Workflow Complete end-to-end MLOps pipeline orchestration from data preparation through model deployment. ## Do not use this skill when - The task is unrelated to ml pipeline workflow - You need a different domain or tool outside this scope ## Instructions - Clarify goals, constraints, and required inputs. - Apply relevant best practices and validate outcomes. - Provide actionable steps and verification. - If detailed examples are required, open `resources/implementation-playbook.md`. ## Overview This skill provides comprehensive guidance for building production ML pipelines that handle the full lifecycle: data ingestion → preparation → training → validation → deployment → monitoring. ## Use this skill when - Building new ML pipelines from scratch - Designing workflow orchestration for ML systems - Implementing data → model → deployment automation - Setting up reproducible training workflows - Creating DAG-based ML orchestration - Integrating ML components into production systems ## What This Skill Provides ### Core Capabilities 1. **Pipeline Architecture** - End-to-end workflow design - DAG orchestration patterns (Airflow, Dagster, Kubeflow) - Component dependencies and data flow - Error handling and retry strategies 2. **Data Preparation** - Data validation and quality checks - Feature engineering pipelines - Data versioning and lineage - Train/validation/test splitting strategies 3. **Model Training** - Training job or...