What orchestration tools does this skill support?

This skill provides architecture patterns and guidance for popular orchestration tools including Apache Airflow, Dagster, Kubeflow Pipelines, and Prefect.

Can I use this for experiment tracking?

Absolutely. It provides patterns for integrating experiment tracking tools like MLflow, Weights & Biases, and TensorBoard into your training workflows.

What deployment strategies are covered?

The skill covers a variety of production serving patterns, including canary deployments, blue-green strategies, shadow deployments, and automated rollback mechanisms.

Does this skill help with data quality and validation?

Yes, it includes specialized sections for data validation, quality checks, and feature engineering to ensure data integrity throughout the pipeline.

ML Pipeline Workflow

Name: ML Pipeline Workflow
Author: Activer007

byActiver007

데이터 과학 및 ML

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

소개

The ML Pipeline Workflow skill provides comprehensive guidance for orchestrating production-grade machine learning lifecycles, covering everything from data ingestion and preparation to model validation and monitoring. It empowers developers to implement robust MLOps practices, including DAG-based orchestration, automated data quality checks, experiment tracking, and advanced deployment strategies like canary or blue-green releases, ensuring that ML systems are reproducible, scalable, and production-ready.

주요 기능

Automated data validation and feature engineering workflows
Experiment tracking integration with MLflow and Weights & Biases
Production deployment patterns including canary and blue-green strategies
Comprehensive model validation and performance regression detection
End-to-end MLOps pipeline orchestration and DAG design
0 GitHub stars

사용 사례

Architecting reproducible ML training pipelines from scratch
Implementing automated data quality checks and feature versioning
Designing CI/CD workflows for seamless model deployment and monitoring

소개

주요 기능

Automated data validation and feature engineering workflows
Experiment tracking integration with MLflow and Weights & Biases
Production deployment patterns including canary and blue-green strategies
Comprehensive model validation and performance regression detection
End-to-end MLOps pipeline orchestration and DAG design
0 GitHub stars

사용 사례

Architecting reproducible ML training pipelines from scratch
Implementing automated data quality checks and feature versioning
Designing CI/CD workflows for seamless model deployment and monitoring