Get Started with MLflow

MLflow is an open-source platform that helps you manage the complete machine learning lifecycle. It enables you to track experiments, organize models, and streamline deployments, bringing structure, consistency, and reproducibility to your ML workflows.

Think of MLflow as your machine learning project manager, similar to how Git tracks code changes, MLflow tracks all critical elements of an ML workflow, including:

• Experiments and training runs – Log and compare different model runs.
• Model parameters and performance metrics – Record inputs and evaluate results consistently.
• Versioned models – Store and manage multiple versions of models for better control and reproducibility.
• Deployment-ready artifacts – Package models and metadata for easy deployment across environments.

Why Use MLflow?

As machine learning (ML) projects scale, they often become difficult to manage. Key questions arise:

• Which hyperparameters produced the best results?
• Where is the trained model from last week?
• How do I deploy this model reliably?

MLflow addresses these challenges by providing a centralized, consistent, and accessible system for tracking all stages of the ML lifecycle. It helps you:

• Log and compare experiments
• Track parameters and metrics
• Version and manage models
• Prepare models for seamless deployment

With MLflow, you bring order to ML development chaos—making your workflows repeatable, transparent, and production-ready.