Does this skill help with cloud-specific ML services?

Yes, it provides integration patterns for major cloud ML services including AWS SageMaker, Google Vertex AI, and Azure ML.

How does this differ from the ml-pipeline-orchestrator skill?

MLOps DAG Builder focuses on general, platform-agnostic MLOps infrastructure and third-party orchestrators, whereas ml-pipeline-orchestrator is specifically for SpecWeave increment-based ML development.

Can I use this for model deployment automation?

Yes, it includes patterns for canary releases, blue-green deployments, and model serving infrastructure configuration.

What orchestration tools are supported by this skill?

This skill supports major orchestration platforms including Apache Airflow, Dagster, Kubeflow Pipelines, and Prefect.

MLOps DAG Builder

Name: MLOps DAG Builder
Author: anton-abyzov

byanton-abyzov

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Ciencia de Datos y ML

Designs and implements production-grade DAG-based MLOps pipeline architectures using orchestrators like Airflow, Dagster, and Kubeflow.

Acerca de

MLOps DAG Builder provides a specialized framework for architecting platform-agnostic machine learning workflows using industry-standard orchestration tools. It enables developers to design modular, idempotent pipelines that cover the entire ML lifecycle—from data validation and feature engineering to model training, evaluation, and automated deployment—ensuring scalable and observable infrastructure across cloud-native environments like AWS SageMaker and Vertex AI.

Características Principales

Standardized patterns for data validation and feature engineering
13 GitHub stars
Production-ready templates for ML training and validation workflows
Multi-platform DAG orchestration for Airflow, Dagster, and Kubeflow
Automated CI/CD integration for model deployment strategies
Support for experiment tracking and model registry integration

Casos de Uso

Building automated retraining pipelines triggered by data drift or schedule
Architecting end-to-end ML workflows on cloud platforms like SageMaker or Vertex AI
Standardizing reusable pipeline templates for cross-functional data science teams

Acerca de

Características Principales

Standardized patterns for data validation and feature engineering
13 GitHub stars
Production-ready templates for ML training and validation workflows
Multi-platform DAG orchestration for Airflow, Dagster, and Kubeflow
Automated CI/CD integration for model deployment strategies
Support for experiment tracking and model registry integration

Casos de Uso

Building automated retraining pipelines triggered by data drift or schedule
Architecting end-to-end ML workflows on cloud platforms like SageMaker or Vertex AI
Standardizing reusable pipeline templates for cross-functional data science teams