MLflow is an open source platform for managing the end-to-end machine learning lifecycle. It has the following primary components: Tracking: Allows you to track experiments to record and compare parameters and results.
Main components are
The tracking component allows you to record machine model training sessions (called runs) and run queries using Java, Python, R, and REST APIs.
The model component provides a standard unit for packaging and reusing machine learning models
The model registry component lets you centrally manage models and their lifecycle.
The project component packages code used in data science projects, ensuring it can easily be reused and experiments can be reproduced.
here are two other key concepts in MLflow:
A run is a collection of parameters, metrics, labels, and artifacts related to the training process of a machine learning model.
An experiment is the basic unit of MLflow organization. All MLflow runs belong to an experiment. For each experiment, you can analyze and compare the results of different runs, and easily retrieve metadata artifacts for analysis using downstream tools. Experiments are maintained on an MLflow tracking server hosted on Azure Databricks.
References:
https://www.run.ai/guides/machine-learning-operations/mlflow
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