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.

💡 What You Will Learn

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.

## 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 | Component | Purpose | |---|---| | **Tracking** | Log params, metrics, and artifacts per run | | **Models** | Package models in a standard format | | **Registry** | Version, stage, and approve models | | **Projects** | Package code for reproducible runs | ## Track your first experiment ```bash pip install mlflow ``` ```python 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: ```bash mlflow ui # Open http://localhost:5000 ``` ## Register and serve a model ```python mlflow.register_model("runs://model", "chat-model") ``` ```bash # 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.