mlflow

name	mlflow
description	MLflow — open-source MLOps platform. Experiment tracking, model registry, packaging, deployment, and evaluation. Multi-cloud ML workflows with reproducible runs and artifact logging.
tags	["mlflow","mlops","experiment-tracking","model-registry","deployment","python","zorai"]

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

References

• MLflow docs
• MLflow GitHub