ML Lifecycle Management

MLflow

MLflow gives you a single platform to track experiments, package models, manage versions, and deploy to production — all open source, framework-agnostic, and designed for how ML teams actually work.

License Apache 2.0

Language Python

Latest Version 3.10.1

GitHub mlflow/mlflow

💡 Core Concepts Beginner Key abstractions — experiments, runs, parameters, metrics, artifacts, models, and the model registry. 🏗 Architecture Intermediate System design — tracking server, backend store, artifact store, model registry, and how they connect. ⚙️ How It Works Intermediate Internal mechanisms — experiment tracking, autologging, model packaging, and deployment pipelines. 💻 Implementation Details Advanced Hands-on — getting started, code patterns, configuration, and annotated source code walkthrough. 📊 Use Cases Beginner – Intermediate When to use MLflow, when not to, and real-world production deployments. 🔌 Ecosystem & Integrations Intermediate Framework flavors, deployment targets, and common tool pairings. ❓ FAQ All Levels Senior-level questions about concurrency, scaling, migration, autologging, and A/B testing. ⚖️ Trade-offs & Limitations Intermediate Honest strengths, limitations, and comparison with W&B, Neptune, DVC, and Kubeflow.

Quick Start

Install MLflow and track your first experiment in under a minute.

python

import mlflow

# Set up experiment and track a training run
mlflow.set_experiment("my-first-experiment")

with mlflow.start_run():
    mlflow.log_param("learning_rate", 0.01)
    mlflow.log_metric("accuracy", 0.95)
    mlflow.sklearn.log_model(model, "model")

Dive into Core Concepts to understand experiments, runs, and the model registry, or jump to Implementation Details for more code patterns.