mlops-mlflow

Topics in the source

• MLflow: ML Lifecycle Management Platform
• When to Use This Skill
• Installation
• Quick Start
• Basic Tracking
• Autologging (Automatic Tracking)
• Core Concepts
• 1. Experiments and Runs

Use MLflow when you need to: Track ML experiments with parameters, metrics, and artifacts Manage model registry with versioning and stage transitions Deploy models to various platforms (local, cloud, serving) Reproduce experiments with project configurations Compare model versions and performance metrics Collaborate on ML projects with team workflows Integrate with any ML framework (framework-agnostic) Users: 20,000+ organizations | GitHub Stars: 23k+ | License: Apache 2.0 Experiment: Logical container for related runs Run: Single execution of ML code (parameters, metrics, artifacts) Automatically log metrics, parameters, and models for popular frameworks. Manage model lifecycle with versioning and stage transitions. Transition models between stages: None → Staging → Production → Archived Find runs programmatically. Documentation: https://mlflow.org/docs/latest GitHub: https://github.com/mlflow/mlflow (23k+ stars) Examples: https://github.com/mlflow/mlflow/tree/master/examples Community: https://mlflow.org/community references/tracking.md - Comprehensive tracking guide references/model-registry.md - Model lifecycle management references/deployment.md - Production deployment pattern

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