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doanchienthangdev
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mlops

Automate ML lifecycle management with CI/CD, experiment tracking, and model registry.

4|1|Updated Dec 30, 2025
Search Tags:#ci/cd#mlops#machine learning#experiment tracking#pipeline orchestration#model registry#data versioning
One-click install
npx skills add https://github.com/doanchienthangdev/omgkit --skill mlops-doanchienthangdev
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: mlops
Source: https://github.com/doanchienthangdev/omgkit/tree/main/plugin/skills/ml-systems/mlops
Command: npx skills add https://github.com/doanchienthangdev/omgkit --skill mlops-doanchienthangdev

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill addresses the complexities of deploying, managing, and monitoring machine learning models in production environments, bridging the gap between development and reliable operation.

Core Features & Use Cases

  • Experiment Tracking: Log parameters, metrics, and artifacts for reproducibility using tools like MLflow and Weights & Biases.
  • Model Registry: Manage model versions, stages (staging, production), and metadata.
  • Pipeline Orchestration: Automate ML workflows with Airflow or Kubeflow Pipelines.
  • CI/CD for ML: Integrate ML model training and deployment into continuous integration and delivery pipelines.
  • Data Versioning: Manage datasets and track changes using DVC.
  • Use Case: Automate the retraining of a fraud detection model weekly, track its performance against the current production model, and automatically deploy it if it meets predefined quality gates.

Quick Start

Use the mlops skill to set up a CI/CD pipeline for your machine learning project.

Frequently Asked Questions about mlops

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I set up a CI/CD pipeline for machine learning models?▼

To set up CI/CD for ML, integrate model training and deployment into continuous delivery pipelines using orchestration tools like Airflow or Kubeflow, automating updates from development to production environments.

What is the best way to track ML experiments for reproducibility?▼

Tracking ML experiments for reproducibility involves logging parameters, metrics, and artifacts using tools like MLflow and Weights & Biases to maintain consistent records across model iterations.

Can I manage model versions and staging environments with MLflow?▼

Yes, you can manage model versions and stages like staging and production using a model registry, handling metadata and lifecycle transitions directly within MLflow.

How does data versioning work with DVC in pipeline orchestration?▼

Data versioning with DVC integrates into pipeline orchestration by managing datasets and tracking changes, ensuring consistent data states across automated ML workflows orchestrated by Airflow or Kubeflow.

When do I need pipeline orchestration for machine learning workflows?▼

Pipeline orchestration is needed for machine learning workflows when automating end-to-end tasks like weekly retraining, performance tracking against production models, and deploying updates through predefined quality gates.

Does this MLOps workflow support automated fraud detection model retraining?▼

Yes, this MLOps workflow supports automated fraud detection model retraining by orchestrating pipelines to track performance against current production models and deploy updates meeting predefined quality gates.

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