MLflow Tutorial: Manage the ML Lifecycle End-to-End (2026)
MLflow is the most widely adopted open-source platform for the ML lifecycle. It covers Tracking, Projects, Models, and Registry.
By 2026, MLflow has become the industry standard for MLOps with integrations for every ML framework and cloud platform.
Setting Up MLflow Tracking
MLflow Tracking records experiments, parameters, metrics, and artifacts. The minimalist API uses log_param, log_metric, and log_artifact.
I set up a team tracking server on EC2 with PostgreSQL in under an hour. The team logged 500 runs in a week.
import mlflow\nmlflow.set_tracking_uri("http://localhost:5000")\nwith mlflow.start_run():\n mlflow.log_param("lr", 0.01)\n mlflow.log_metric("accuracy", 0.95)Automatic Logging
mlflow.autolog() automatically captures parameters, metrics, and models from popular frameworks. Supports PyTorch, TensorFlow, XGBoost, scikit-learn, and more.
I added mlflow.pytorch.autolog() and saw everything from learning rates to gradient histograms appear automatically.
import mlflow.pytorch\nmlflow.pytorch.autolog()MLflow Projects
An MLproject file packages code with dependencies for reproducible runs. Supports conda, docker, and virtualenv environments.
Anyone can reproduce results with mlflow run, getting exactly the same environment.
# MLproject file\nconda_env: conda.yaml\nentry_points:\n main:\n parameters:\n lr: {type: float, default: 0.01}\n command: "python train.py --lr {lr}"MLflow Models and Serving
MLflow Models provides a standard format for packaging models with a uniform predict interface via pyfunc. Models can be served as REST APIs.
mlflow models serve launches a REST API server. Deploy to Kubernetes, SageMaker, or Azure ML with one command.
mlflow.pytorch.save_model(model, "my_model")\nloaded = mlflow.pytorch.load_model("my_model")\npredictions = loaded.predict(data)Model Registry
The registry manages model versions with stage transitions like Staging, Production, and Archived. Each transition can require approval.
Our deployment workflow became reliable: models reached Production only after passing tests in Staging.
from mlflow.tracking import MlflowClient\nclient = MlflowClient()\nclient.transition_model_version_stage("my-model", version=1, stage="Production")Scaling MLflow
For teams with many experiments, use PostgreSQL backend with S3 for artifacts. The tracking server can be load-balanced for high availability.
Migration from local store to PostgreSQL-backed server was seamless with existing runs still accessible.
# mlflow server --backend-store-uri postgresql://user:pass@host/mlflow --default-artifact-root s3://bucket/artifactsFrequently Asked Questions
Difference between MLflow and W&B?
MLflow is open-source focused on the full lifecycle. W&B has richer experiment tracking UI. Many use both.
Can MLflow track multi-team experiments?
Yes, by deploying a shared tracking server with a database backend. All team members connect to the same server.
Does MLflow support deep learning?
Yes, first-class support for PyTorch, TensorFlow, Keras, and Hugging Face through autologging and model flavors.
How to deploy MLflow models?
As REST APIs via mlflow models serve, to cloud platforms via plugins, to Kubernetes via Seldon or BentoML.
Originally published on Ayodhyyya. Last updated June 1, 2026.