What orchestration tools does this skill support?

It provides patterns and guidance for popular tools including Apache Airflow, Dagster, Kubeflow Pipelines, and Prefect.

Does it help with model monitoring after deployment?

Absolutely; it covers monitoring for model drift, performance regression detection, and production traffic validation.

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 features.

Is it compatible with major cloud providers?

Yes, it includes integration strategies for AWS SageMaker, Google Vertex AI, and Azure ML, as well as cloud-agnostic Kubernetes setups.

ML Pipeline Workflow

Name: ML Pipeline Workflow
Author: camoneart

bycamoneart

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Data Science & ML

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

About

The ML Pipeline Workflow skill provides comprehensive guidance and standardized patterns for building robust, production-grade machine learning systems. It helps developers and data scientists move beyond manual notebooks by implementing automated DAG-based orchestration, data validation, experiment tracking, and sophisticated deployment strategies like canary and blue-green releases. Whether you are using Airflow, Kubeflow, or cloud-native tools like SageMaker, this skill ensures your ML lifecycle is reproducible, observable, and scalable.

Key Features

Integration with experiment tracking tools like MLflow and W&B
Automated data validation and feature engineering pipeline templates
Robust model validation frameworks for performance regression detection
End-to-end DAG orchestration patterns for complex ML workflows
3 GitHub stars
Advanced deployment automation including canary and blue-green strategies

Use Cases

Building a reproducible training pipeline for a new deep learning model
Implementing automated data quality checks and feature versioning in production
Designing a continuous training workflow triggered by model performance drift

About

Key Features

Integration with experiment tracking tools like MLflow and W&B
Automated data validation and feature engineering pipeline templates
Robust model validation frameworks for performance regression detection
End-to-end DAG orchestration patterns for complex ML workflows
3 GitHub stars
Advanced deployment automation including canary and blue-green strategies

Use Cases

Building a reproducible training pipeline for a new deep learning model
Implementing automated data quality checks and feature versioning in production
Designing a continuous training workflow triggered by model performance drift