Automating model tracking with MLflow
As we mentioned previously, MLflow is an open-source platform for managing machine and deep learning model life cycles, which allows us to perform experiments, ensure reproducibility, and support easy model deployment. It also provides us with a centralized model registry. As a general overview, the components of MLflow are as follows:
- MLflow Tracking: It records all data associated with an experiment, such as code, data, configuration, and results.
- MLflow Projects: It wraps the code in a format that ensures the results can be reproduced between runs, regardless of the platform.
- MLflow Models: This provides us with a deployment platform for our machine learning and deep learning models.
- Model Registry: The central repository for our machine learning and deep learning models.
In this section, we will focus on MLflow Tracking, which is the component that allows us to log and register the code, properties, hyperparameters...