Mlops Workflows

This is a solid reference for building production ML systems with MLflow. It covers the full lifecycle: experiment tracking with nested runs for hyperparameter tuning, model registry with versioning and aliases, and deployment patterns. The code examples are practical, showing autologging, metric tracking over epochs, and artifact management. It's most useful when you're moving from Jupyter notebooks to repeatable workflows or need to standardize how your team tracks experiments. The nested run pattern for grid search is especially handy. Missing some depth on the monitoring and feature store sections based on the preview, but the core MLflow workflows are well documented.

skills.sh installs: 445
GitHub stars: 63
last updated: Aug 21, 2026

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Install

$ npx -y skills add manutej/luxor-claude-marketplace --skill mlops-workflows --agent claude-code

Installs into .claude/skills of the current project.

Files

MLOps Workflows with MLflow

A comprehensive guide to production-grade MLOps workflows covering the complete machine learning lifecycle from experimentation to production deployment and monitoring.

MLflow Components Overview
Experiment Tracking
Model Registry
Deployment Patterns
Monitoring and Observability
A/B Testing
Feature Stores
CI/CD for ML
Model Versioning
Production Best Practices

MLflow Components Overview

MLflow consists of four primary components for managing the ML lifecycle:

1. MLflow Tracking

Track experiments, parameters, metrics, and artifacts during model development.

import mlflow

# Set tracking URI
mlflow.set_tracking_uri("http://localhost:5000")

# Create or set experiment
mlflow.set_experiment("production-models")

SKILL.md on GitHub

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