Managing the ML Lifecycle using MLflow¶
The ML lifecycle covers the following stages of a ML engineering project:
- data preparation
MLflow is an open-source framework that supports the ML lifecycle by tracking training metrics, storing trained models and model deployment.
- How to setup and configure MLflow.
- How to track metrics.
- How to track metrics during hyper-parameter optimisation.
- How to save a model to the MLflow registry.
- How to retrieve the latest model tagged for production.
- How to serve model predictions via a REST API.
Running the Demo¶
This demo is contained within a single Jupyter notebook -
demos/mlflow/mlflow_basics.ipynb. Make sure you have the necessary Python package requirements installed into a Jupyter kernel for it to run successfully.