Which orchestration tools does this skill support?

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

Can I use this for experiment tracking?

Absolutely. It offers integration patterns for popular experiment tracking tools like MLflow and Weights & Biases to manage metrics and model artifacts.

Does it include data validation features?

Yes, it provides frameworks for data quality checks, feature engineering pipelines, and lineage tracking to ensure data integrity throughout the pipeline.

How does this skill handle model deployment?

It includes standardized templates and strategies for various serving patterns, specifically canary deployments, blue-green releases, and shadow deployments.

MLOps Pipeline Orchestrator

Name: MLOps Pipeline Orchestrator
Author: HermeticOrmus

byHermeticOrmus

数据科学与机器学习

Build, automate, and manage end-to-end MLOps pipelines from data ingestion through production deployment.

关于

This skill empowers developers and data scientists to construct robust, production-grade MLOps pipelines by providing standardized patterns for orchestration, data validation, and automated deployment. It streamlines the transition from experimental models to production environments by implementing best practices for DAG-based workflows, experiment tracking, and model monitoring, ensuring reproducible and scalable machine learning operations across various cloud infrastructures.

主要功能

Comprehensive performance regression and model drift monitoring
0 GitHub stars
Integrated experiment tracking and model registry management
End-to-end DAG orchestration design for Airflow, Dagster, and Kubeflow
Advanced deployment strategies including Canary and Blue-Green releases
Automated data validation and feature engineering workflow patterns

使用场景

Automating repetitive model retraining and validation cycles
Standardizing ML production deployments across distributed engineering teams
Implementing reproducible data lineage and dataset versioning systems

关于

主要功能

Comprehensive performance regression and model drift monitoring
0 GitHub stars
Integrated experiment tracking and model registry management
End-to-end DAG orchestration design for Airflow, Dagster, and Kubeflow
Advanced deployment strategies including Canary and Blue-Green releases
Automated data validation and feature engineering workflow patterns

使用场景

Automating repetitive model retraining and validation cycles
Standardizing ML production deployments across distributed engineering teams
Implementing reproducible data lineage and dataset versioning systems