mkurman Overview
MLflow is the leading open-source MLOps platform covering experiment tracking, model registry, packaging (MLflow Models format), and deployment (MLflow Serving). Supports PyTorch, TensorFlow, scikit-learn, ONNX, XGBoost, and custom models across cloud and on-prem.
Installation
uv pip install mlflow
Experiment Tracking
import mlflow
mlflow.set_experiment("my_project")
with mlflow.start_run(run_name="experiment_1"):
mlflow.log_param("learning_rate", 0.01)
mlflow.log_param("batch_size", 32)
mlflow.log_metric("accuracy", 0.92)
mlflow.log_metric("loss", 0.35)
mlflow.log_artifact("model.pth")
mlflow.pytorch.log_model(model, "model")
Model Registry & Serving
mlflow.register_model("runs:/<run_id>/model", "MyModel")
mlflow models serve --model-uri models:/MyModel/1 --port 5001
mlflow ui --host 0.0.0.0 --port 5000