Does it include model monitoring capabilities?

Absolutely. It covers post-deployment monitoring for model drift, performance degradation, and infrastructure metrics like latency and throughput.

How does it handle data versioning?

It implements industry best practices for data lineage and versioning using tools like DVC to ensure every model training run is reproducible.

What orchestration tools does this skill support?

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

Can I use this for real-time feature engineering?

Yes, the skill includes architectural patterns for both batch training pipelines and real-time stream processing for feature engineering.

MLOps Pipeline Workflow

Name: MLOps Pipeline Workflow
Author: yusoofsh

byyusoofsh

数据科学与机器学习

Builds end-to-end MLOps pipelines to automate data preparation, model training, validation, and production deployment.

关于

This skill provides a comprehensive framework for architecting and implementing production-grade MLOps workflows. It guides users through the entire machine learning lifecycle, from data ingestion and feature engineering to experiment tracking and automated deployment strategies like canary or blue-green releases. By incorporating best practices for modularity, idempotency, and observability, it helps developers create reproducible, scalable, and monitorable ML systems using industry-standard tools like Airflow, Kubeflow, and MLflow.

主要功能

Model validation frameworks with performance regression detection
0 GitHub stars
Automated data validation and versioned feature engineering
End-to-end DAG orchestration for complex ML workflows
Experiment tracking and model registry integration
Advanced deployment patterns including canary and A/B testing

使用场景

Setting up automated retraining workflows triggered by data drift
Designing a reproducible training pipeline for production models
Implementing a model-to-serving pipeline with integrated quality gates

关于

主要功能

Model validation frameworks with performance regression detection
0 GitHub stars
Automated data validation and versioned feature engineering
End-to-end DAG orchestration for complex ML workflows
Experiment tracking and model registry integration
Advanced deployment patterns including canary and A/B testing

使用场景

Setting up automated retraining workflows triggered by data drift
Designing a reproducible training pipeline for production models
Implementing a model-to-serving pipeline with integrated quality gates