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mlops

Manage ML lifecycle with MLflow tracking, registry, and deployment pipelines.

5|1|Updated Nov 18, 2025
Search Tags:#mlops#mlflow#experiment-tracking#model-registry#deployment-pipeline#production-ml
One-click install
npx skills add https://github.com/pluginagentmarketplace/custom-plugin-data-engineer --skill mlops-pluginagentmarketplace
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: mlops
Source: https://github.com/pluginagentmarketplace/custom-plugin-data-engineer/tree/main/skills/mlops
Command: npx skills add https://github.com/pluginagentmarketplace/custom-plugin-data-engineer --skill mlops-pluginagentmarketplace

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires yaml, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

Production ML systems require robust tracking, versioning, and deployment capabilities to move models from development to production safely and reproducibly. The mlops skill provides end-to-end tooling and best practices to manage experiments, track artifacts, and automate deployment pipelines.

Core Features & Use Cases

  • Experiment tracking with MLflow to capture parameters, metrics, and lineage of experiments.
  • Model registry and versioning to promote models through stages (e.g., Staging, Production) with traceable runs.
  • Deployment pipelines and serving patterns (FastAPI/BentoML) enabling scalable inference in production environments.
  • Use Case: A data science team iterates on a churn-model, logs experiments, registers the best model, and automatically deploys it to a production endpoint.

Quick Start

Use the mlops skill to set up an MLflow tracking server, run a small experiment that logs parameters and metrics, register the model, and deploy a serving endpoint.

Frequently Asked Questions about mlops

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

FAQPage Schema
How do I track ML experiments and parameters with MLflow?▼

MLflow experiment tracking captures parameters, metrics, and lineage for machine learning runs. You log values during execution to build a reproducible history of model iterations and artifacts.

What's the best way to version and register machine learning models for production?▼

Model registry and versioning promote models through stages like Staging and Production with traceable runs. This tracks artifact lineage and ensures safe, reproducible deployment of machine learning models.

Can I use FastAPI or BentoML to serve machine learning models in production?▼

FastAPI and BentoML enable scalable inference in production environments. They provide deployment pipelines and serving patterns to expose machine learning models as endpoints for live predictions.

Do I need YAML to set up deployment pipelines for machine learning?▼

YAML is required to configure deployment pipelines and orchestrate CI/CD processes. It defines the environment and stage transitions needed to move machine learning models safely into production.

How does MLOps handle the end-to-end machine learning lifecycle?▼

MLOps manages the end-to-end machine learning lifecycle by integrating experiment tracking, model registry, and deployment pipelines. It orchestrates transitions from development to production serving safely and reproducibly.

What is the workflow for deploying a churn model from experiments to a production endpoint?▼

The workflow involves logging experiments with MLflow, registering the best performing model, and automatically deploying it to a production endpoint. This creates a traceable path from iteration to serving.

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