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mlops-expert

Set up and monitor ML pipelines with MLflow, DVC, FastAPI, and Feast.

Updated May 4, 2026
Search Tags:#ci/cd#mlops#mlflow#model registry#feature store#model serving#data versioning
One-click install
npx skills add https://github.com/luokai25/luo-ai-skills-market --skill mlops-expert
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: mlops-expert
Source: https://github.com/luokai25/luo-ai-skills-market/tree/main/09-data-and-ai%20%28by%20Luo%20Kai%29/05-mlops/mlops-expert
Command: npx skills add https://github.com/luokai25/luo-ai-skills-market --skill mlops-expert

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires mlflow, dvc, fastapi, feast, pandas, numpy, scikit-learn, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill provides expert-level MLOps solutions for setting up and monitoring ML pipelines in production, including experiment tracking, model registry, serving, feature stores, and more.

Core Features & Use Cases

  • Experiment Tracking: Utilizes MLflow for tracking experiments, logging metrics, and versioning models.
  • Model Registry: Manages model versions through stages, from training to production.
  • Model Serving: Implements model serving with FastAPI and MLflow models.
  • Feature Stores: Incorporates feature stores like Feast for online/offline feature serving.
  • Data Versioning: Uses DVC for data versioning and reproducibility.
  • CI/CD for ML: Automates training pipelines and validation gates with CI/CD pipelines.
  • Model Monitoring: Detects data and concept drift, monitors predictions, and alerts.

Quick Start

Use the mlops-expert skill to set up an ML pipeline for your project with the following command: setup-ml-pipeline -d /path/to/dataset -m /path/to/model.

Frequently Asked Questions about mlops-expert

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

FAQPage Schema
How do I set up an ML pipeline with MLflow and DVC for production?▼

To set up an ML pipeline, use this Skill to orchestrate MLflow for experiment tracking and DVC for data versioning, providing reproducible training workflows and model versioning for production deployment.

What is the best way to manage feature stores for online and offline serving?▼

The best way to manage feature stores is using Feast to handle online and offline feature serving, ensuring consistent data features are available for both training pipelines and production model serving.

Can I use FastAPI to serve MLflow models in production?▼

Yes, you can use FastAPI to serve MLflow models by implementing a serving layer that loads registered model versions from the MLflow model registry and exposes them as live prediction endpoints.

How does CI/CD for machine learning automate training and validation gates?▼

CI/CD for machine learning automates training pipelines by triggering continuous integration jobs that run data validation gates, retrain models on versioned data, and promote validated artifacts to the model registry.

Do I need Python and scikit-learn to monitor ML pipelines for data drift?▼

Yes, you need Python with pandas, numpy, and scikit-learn to execute model monitoring scripts that detect data and concept drift, analyze production predictions, and trigger alerts for pipeline degradation.

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