How does it handle experiment tracking?

It integrates with tracking platforms like MLflow and Weights & Biases to manage hyperparameters, visualize metrics, and maintain a model registry.

What deployment strategies are covered?

The skill covers several production-ready patterns such as shadow deployments, canary releases, blue-green strategies, and automated rollback mechanisms.

Does it include templates for data preparation?

It provides comprehensive templates and checklists for data validation, quality checks, feature transformations, and data lineage tracking.

Can this skill help with model monitoring?

Yes, it includes guidance on setting up observability, logging metrics at every stage, and monitoring for model performance drift after deployment.

What orchestration tools does this skill support?

The skill provides patterns and templates for popular industry-standard tools including Apache Airflow, Dagster, Kubeflow Pipelines, and Prefect.

ML Pipeline Workflow

Name: ML Pipeline Workflow
Author: Microck

byMicrock

•

Ciencia de Datos y ML

Build and automate end-to-end MLOps pipelines from data preparation and training through to production deployment.

The ML Pipeline Workflow skill provides a comprehensive framework for orchestrating production-grade machine learning lifecycles. It guides users through the critical stages of data ingestion, feature engineering, model training, and automated validation, ensuring that every step is reproducible and scalable. By implementing standardized patterns for DAG-based orchestration and experiment tracking, this skill helps developers bridge the gap between experimental notebooks and robust production systems using tools like Airflow, Kubeflow, and MLflow.

Características Principales

01Automated model validation and experiment tracking setup

02Production deployment strategies including canary and blue-green releases

03Integrated data validation and feature engineering pipelines

0481 GitHub stars

05End-to-end MLOps orchestration from ingestion to deployment

06DAG orchestration patterns for Airflow, Dagster, and Kubeflow

Casos de Uso

01Automating recurring model retraining and deployment workflows

02Architecting a new production-ready ML pipeline from scratch

03Implementing data drift detection and lineage tracking for existing models

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add microck/ordinary-claude-skills ml-pipeline-workflow

For use in Claude.ai and ChatGPT

Download Skill