MLflow (27,379 Stars) 2026: The Open Platform for the Full AI Engineering Lifecycle - Tracking, Models, Registry

MLflow (27,379 stars) is the open-source platform for the full AI engineering lifecycle - experiment tracking, model packaging and the model registry. Here is the complete workflow.

📜 Table of Contents

• The short answer
• The four components
• Track your first experiment
• Register and serve a model
• Practical tips
• FAQ

The short answer

mlflow/mlflow (27,379 stars, Python) is an open-source platform for the machine learning and AI engineering lifecycle. It covers experiment tracking, model packaging, deployment, and a central model registry - the de facto standard for managing LLM and ML experiments across teams.

The four components

Track your first experiment

pip install mlflow

import mlflow

mlflow.set_experiment("llm-finetuning")

with mlflow.start_run():
    mlflow.log_param("model", "qwen2.5-7b")
    mlflow.log_param("lora_r", 16)
    mlflow.log_metric("eval_accuracy", 0.91)
    mlflow.log_artifact("results.json")

View the UI:

mlflow ui
# Open http://localhost:5000

Register and serve a model

mlflow.register_model("runs:/<run_id>/model", "chat-model")

# Serve the registered model as a REST API
mlflow models serve -m "models:/chat-model/Production" --port 5001

Practical tips

• Log prompts and responses too - MLflow has native LLM tracking support.
• Use the registry stages (Staging/Production) for controlled rollouts.
• Back up the tracking store (SQLite/file) along with your artifact store.

FAQ

Is it free? Yes - Apache-2.0 open source.

Does it support LLM workflows? Yes - MLflow 2.x+ added first-class LLM tracking, evaluation and prompt management.

Can it run on Kubernetes? Yes - the tracking server and model serving scale on k8s.

❓ FAQ

Does it support LLM workflows?

Yes - MLflow 2.x+ added first-class LLM tracking, evaluation and prompt management.

Can it run on Kubernetes?

Yes - the tracking server and model serving scale on k8s.