What orchestration tools are supported by this skill?

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

How does the skill handle model validation?

It provides frameworks for performance regression detection, A/B testing infrastructure, and pre-deployment validation checklists to ensure model quality.

Does it support automated model deployment?

Yes, it covers various production deployment patterns such as canary releases, blue-green deployments, and automated rollback mechanisms.

Can this skill help with data versioning?

Yes, it includes best practices for data versioning and lineage tracking using tools like DVC to ensure reproducibility across the pipeline.

ML Pipeline Workflow

Name: ML Pipeline Workflow
Author: apassuello

byapassuello

0•

データサイエンスとML

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

The ML Pipeline Workflow skill provides a standardized framework for building robust, production-grade machine learning pipelines. It guides developers through the entire MLOps lifecycle, including data validation, feature engineering, model training, and automated deployment strategies like canary or blue-green releases. By implementing DAG-based orchestration patterns and experiment tracking, this skill ensures that ML workflows are reproducible, scalable, and observable, making it ideal for teams transitioning from experimental notebooks to integrated production systems.

主な機能

01Experiment tracking and model registry integration

02Comprehensive monitoring for model drift and performance metrics

03Automated data validation and feature engineering pipelines

04Advanced deployment strategies including Canary and Blue-Green

05DAG-based orchestration patterns for Airflow, Dagster, and Kubeflow

060 GitHub stars

ユースケース

01Transitioning experimental ML code into a production-ready automated workflow

02Implementing CI/CD for machine learning (MLOps) with automated validation gates

03Setting up reproducible training pipelines with data lineage and experiment tracking

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add apassuello/multimodal_insight_engine ml-pipeline-workflow

For use in Claude.ai and ChatGPT

主な機能

01Experiment tracking and model registry integration

02Comprehensive monitoring for model drift and performance metrics

03Automated data validation and feature engineering pipelines

04Advanced deployment strategies including Canary and Blue-Green

05DAG-based orchestration patterns for Airflow, Dagster, and Kubeflow

060 GitHub stars

ユースケース

01Transitioning experimental ML code into a production-ready automated workflow

02Implementing CI/CD for machine learning (MLOps) with automated validation gates

03Setting up reproducible training pipelines with data lineage and experiment tracking

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add apassuello/multimodal_insight_engine ml-pipeline-workflow

For use in Claude.ai and ChatGPT