Does it support advanced production deployment strategies?

Absolutely, it covers sophisticated patterns like canary releases, blue-green deployments, A/B testing, and automated rollback mechanisms.

What orchestration tools does this skill support?

It provides patterns and templates for popular orchestration tools including Apache Airflow, Dagster, Kubeflow Pipelines, and Prefect.

How does it handle data quality and validation?

The skill includes specialized workflows for data validation and quality checks to ensure data integrity and lineage before training begins.

Can I use this for experiment tracking?

Yes, it offers integration patterns for leading experiment tracking tools such as MLflow, Weights & Biases, and TensorBoard.

Is this skill suitable for continuous training (CT)?

Yes, it provides guidance on setting up automated retraining schedules and triggers based on model performance or data drift detection.

ML Pipeline Workflow

Name: ML Pipeline Workflow
Author: HermeticOrmus

byHermeticOrmus

•

Ciencia de Datos y ML

Orchestrates end-to-end MLOps pipelines from data preparation and model training to production deployment and monitoring.

This skill enables developers to architect and automate robust MLOps lifecycles using industry-standard practices. It provides comprehensive guidance and templates for designing Directed Acyclic Graphs (DAGs), automating data validation, tracking experiments with tools like MLflow, and implementing sophisticated deployment strategies such as canary and blue-green releases. By focusing on modularity and observability, it helps teams move from experimental notebooks to reproducible, production-grade machine learning systems.

Características Principales

01Production-ready deployment patterns including canary and A/B testing

02End-to-end DAG orchestration for tools like Airflow and Kubeflow

03Automated data validation and feature engineering workflows

04Integrated experiment tracking and model registry management

054 GitHub stars

06Comprehensive model validation and performance regression detection

Casos de Uso

01Building reproducible ML training pipelines from scratch

02Implementing continuous training and monitoring for model drift

03Automating data-to-model-to-deployment workflows in production

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add hermeticormus/claude-code-game-development ml-pipeline-workflow

For use in Claude.ai and ChatGPT

Download Skill