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Managing the ML Lifecycle using MLflow

The ML lifecycle covers the following stages of a ML engineering project:

  • data preparation
  • training
  • deployment

MLflow is an open-source framework that supports the ML lifecycle by tracking training metrics, storing trained models and model deployment.

Demo Objectives

  • 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.