What deployment strategies are included?

It covers multiple production patterns including shadow deployments, canary releases, blue-green strategies, and automated rollback mechanisms.

Can this skill help with data versioning?

Yes, it includes best practices for data versioning and lineage, helping you integrate tools like DVC or TFX into your pipeline to ensure experiment reproducibility.

Which orchestration tools does this skill support?

The skill provides guidance and templates compatible with major orchestration tools including Apache Airflow, Dagster, Kubeflow Pipelines, and Prefect.

Does it cover model monitoring in production?

Yes, it provides patterns for setting up observability, tracking model performance drift, and monitoring serving metrics like latency and throughput.

ML Pipeline & MLOps Workflow

Name: ML Pipeline & MLOps Workflow
Author: Activer007

byActiver007

数据科学与机器学习

Orchestrates end-to-end MLOps pipelines from data preparation and model training to production deployment and monitoring.

关于

This skill streamlines the creation of production-grade Machine Learning pipelines by providing standardized patterns for data ingestion, feature engineering, model validation, and deployment. It helps developers and data scientists implement MLOps best practices using DAG-based orchestration, versioned datasets, and automated testing to ensure reproducible, scalable, and observable AI workflows. Whether you are building from scratch or automating existing models, this skill provides the templates and guidance needed to transition from experimental notebooks to robust production systems.

主要功能

Automated model validation and A/B testing frameworks
End-to-end DAG orchestration patterns for Airflow, Dagster, and Kubeflow
Experiment tracking integration with MLflow and Weights & Biases
Data validation and feature engineering pipeline templates
Advanced deployment strategies including canary and blue-green rollouts
0 GitHub stars

使用场景

Automating model retraining and deployment triggered by data drift
Building reproducible training pipelines for new machine learning models
Designing scalable orchestration for complex multi-stage ML systems

关于

主要功能

Automated model validation and A/B testing frameworks
End-to-end DAG orchestration patterns for Airflow, Dagster, and Kubeflow
Experiment tracking integration with MLflow and Weights & Biases
Data validation and feature engineering pipeline templates
Advanced deployment strategies including canary and blue-green rollouts
0 GitHub stars

使用场景

Automating model retraining and deployment triggered by data drift
Building reproducible training pipelines for new machine learning models
Designing scalable orchestration for complex multi-stage ML systems