Can I use this for real-time feature engineering?

Yes, it includes guidance for both batch training pipelines and streaming feature processing architectures to support real-time requirements.

What orchestration tools does this skill support?

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

How does it handle data versioning and lineage?

It integrates strategies for data versioning using tools like DVC and emphasizes maintaining clear data lineage throughout the pipeline transformation steps.

Does it help with model monitoring after deployment?

Absolutely. It covers best practices for monitoring model drift, performance degradation, and production traffic latency.

ML Pipeline Workflow

Name: ML Pipeline Workflow
Author: Microck

byMicrock

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Data Science & ML

Builds end-to-end MLOps pipelines for data preparation, model training, validation, and production deployment.

About

This skill empowers developers to architect and implement production-ready machine learning pipelines using MLOps best practices. It provides structured guidance on workflow orchestration via DAGs, automated data validation, experiment tracking, and robust deployment strategies like canary or blue-green releases. By integrating tools such as Airflow, Kubeflow, and MLflow, it ensures that the entire ML lifecycle—from data ingestion to performance monitoring—is reproducible, scalable, and highly observable.

Key Features

Advanced deployment patterns including Canary and Blue-Green
End-to-end MLOps lifecycle orchestration and DAG design
81 GitHub stars
Automated data validation and feature engineering pipelines
Experiment tracking and model registry integration
Model performance regression and drift monitoring

Use Cases

Automating data quality checks and feature transformations
Architecting a reproducible training pipeline from scratch
Implementing continuous training and automated model rollouts

About

Key Features

Advanced deployment patterns including Canary and Blue-Green
End-to-end MLOps lifecycle orchestration and DAG design
81 GitHub stars
Automated data validation and feature engineering pipelines
Experiment tracking and model registry integration
Model performance regression and drift monitoring

Use Cases

Automating data quality checks and feature transformations
Architecting a reproducible training pipeline from scratch
Implementing continuous training and automated model rollouts