The kedro-mlflow plugin#

kedro-mlflow is a Kedro plugin to integrate MLflow effortlessly inside Kedro projects.

Its main features are automatic parameters tracking, datasets tracking as artifacts, Kedro pipelines packaging and serving and automatic synchronisation between training and inference pipelines. It aims at providing a complete yet modular framework for high reproducibility of machine learning experiments and ease of deployment.

Experiment tracking

Track the parameters, metrics, artifacts and models of your kedro pipelines for reproducibility.

source/03_experiment_tracking/01_experiment_tracking/01_configuration.html

Pipeline as model

Package any kedro pipeline to a custom mlflow model for deployment and serving. The custom model for an inference pipeline can be registered in mlflow automatically at the end of each training in a scikit-learn like way.

source/04_pipeline_as_model/01_pipeline_as_custom_model/01_mlflow_models.html

Resources#

Quickstart

Get started in 1 mn with experiment tracking!

source/02_gettnig_started/01_installation/01_installation.html

Advanced tutorial

The kedro-mlflow-tutorial github repo contains a step-by-step tutorial to learn how to use kedro-mlflow as a mlops framework!

https://github.com/Galileo-Galilei/kedro-mlflow-tutorial

Demonstration in video

A youtube video by the kedro team to introduce the plugin, with live coding.

https://www.youtube.com/watch?v=Az_6UKqbznw