Agent skill · software engineering · aiskillstore

ml-pipeline-workflow

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.

Why this skill is useful

Adds executable templates and detailed workflows for building and deploying MLOps pipelines that are not commonly found in public documentation.

What it needs

Requires airflow, dagster, kubeflow installed locally. About 4k tokens when loaded. Last updated 2026-08-07. 405 stars on the source repository.

What this skill does

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 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 orchestration Hyperparameter management Experiment tracking integration Distributed training patterns 4. Model Validation Validation frameworks and metrics A/B testing infrastructure Performance regression detection Model comparison workflows 5. …

How to use it

Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:

@skills aiskillstore/ml-pipeline-workflow--475f0e

View the source on GitHub

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