How does it manage model deployment?

The skill supports various serving patterns including shadow deployments, canary releases, and blue-green strategies to ensure safe production rollouts.

What orchestration tools does this skill support?

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

Can I use this for experiment tracking?

Yes, it includes specific integration points for industry-standard tools like MLflow, Weights & Biases, and TensorBoard.

Is this skill suitable for both simple and complex projects?

Yes, it uses a progressive disclosure approach, starting with simple linear pipelines and scaling up to complex multi-model ensemble strategies.

Does it handle data versioning and lineage?

It offers comprehensive guidance on implementing data versioning and lineage tracking using tools like DVC to ensure pipeline reproducibility.

ML Pipeline Workflow

Name: ML Pipeline Workflow
Author: HermeticOrmus

byHermeticOrmus

Ciencia de Datos y ML

Orchestrates end-to-end MLOps pipelines from data preparation through model training, validation, and production deployment.

Acerca de

This skill provides comprehensive guidance for building production-grade ML pipelines using industry-standard orchestration patterns like Airflow, Dagster, and Kubeflow. It helps developers and data scientists automate the complete machine learning lifecycle, ensuring reproducible workflows, robust data validation, experiment tracking, and seamless deployment strategies such as canary or blue-green releases.

Características Principales

0 GitHub stars
Advanced deployment patterns including canary and A/B testing
Experiment tracking and model registry integration
Comprehensive model validation and regression detection
End-to-end DAG orchestration and workflow design
Automated data validation and feature engineering pipelines

Casos de Uso

Building reproducible training workflows from scratch
Automating data drift detection and model retraining triggers
Implementing CI/CD for machine learning models (MLOps)

Acerca de

Características Principales

0 GitHub stars
Advanced deployment patterns including canary and A/B testing
Experiment tracking and model registry integration
Comprehensive model validation and regression detection
End-to-end DAG orchestration and workflow design
Automated data validation and feature engineering pipelines

Casos de Uso

Building reproducible training workflows from scratch
Automating data drift detection and model retraining triggers
Implementing CI/CD for machine learning models (MLOps)