What MLflow Meaning, Applications & Example
Platform for managing the machine learning lifecycle.
What is MLflow?
MLflow is an open-source platform for managing the end-to-end machine learning lifecycle. It provides tools for tracking experiments, packaging code into reproducible runs, and sharing and deploying models. MLflow supports multiple machine learning libraries, making it flexible for various workflows.
Components of MLflow
- MLflow Tracking: Tracks experiments, logging parameters, metrics, and models during training.
- MLflow Projects: Packaging code into a format that can be shared and reproduced, making it easier to manage machine learning workflows.
- MLflow Models: Allows you to package and deploy models in various formats like TensorFlow , PyTorch , and Scikit-learn .
- MLflow Registry: A centralized model store to manage and deploy models across different environments.
Example of MLflow Tracking
import mlflow
# Start an MLflow run
with mlflow.start_run():
# Log parameters, metrics, and models
mlflow.log_param("learning_rate", 0.01)
mlflow.log_metric("accuracy", 0.95)
mlflow.sklearn.log_model(model, "model")