What orchestration tools does this skill support?

The skill provides patterns and guidance for popular orchestration frameworks including Apache Airflow, Dagster, Kubeflow Pipelines, and Prefect.

Does this skill help with model monitoring?

Yes, it includes patterns for performance regression detection, data drift monitoring, and production observability for deployed models.

Can I use this for cloud-specific ML services?

Absolutely. It covers integration points for AWS SageMaker, Google Vertex AI, and Azure ML, as well as cloud-agnostic Kubernetes deployments.

Does it support automated testing for models?

Yes, it provides validation frameworks to run test suites and compare new models against baselines before they are approved for deployment.

How does it handle data versioning?

The skill implements best practices for data lineage and versioning, often utilizing tools like DVC or cloud-native dataset versioning features.

Machine Learning Pipeline Workflow

Name: Machine Learning Pipeline Workflow
Author: HermeticOrmus

byHermeticOrmus

Data Science & ML

Builds and orchestrates end-to-end MLOps pipelines from data preparation and training through to production deployment.

About

This skill enables Claude to design and implement robust MLOps architectures that bridge the gap between model development and production. It provides comprehensive guidance on creating reproducible workflows, covering data validation, feature engineering, experiment tracking, and automated deployment strategies. Whether you are using Airflow, Kubeflow, or cloud-native tools like SageMaker, this skill ensures your ML pipelines are modular, observable, and scalable, incorporating best practices for model versioning and performance monitoring.

Key Features

0 GitHub stars
End-to-end DAG orchestration patterns and templates
Data validation and feature engineering pipeline design
Automated model validation and A/B testing infrastructure
Production deployment strategies including canary and blue-green
Experiment tracking and model registry integration

Use Cases

Designing modular MLOps infrastructure for team-wide experiment tracking
Building a reproducible training-to-deployment pipeline for a new ML model
Implementing automated retraining workflows triggered by data drift

About

Key Features

0 GitHub stars
End-to-end DAG orchestration patterns and templates
Data validation and feature engineering pipeline design
Automated model validation and A/B testing infrastructure
Production deployment strategies including canary and blue-green
Experiment tracking and model registry integration

Use Cases

Designing modular MLOps infrastructure for team-wide experiment tracking
Building a reproducible training-to-deployment pipeline for a new ML model
Implementing automated retraining workflows triggered by data drift