From 73583ac5045d1d342767ab5a3021af312a1a6278 Mon Sep 17 00:00:00 2001 From: Galileo Galilei Date: Thu, 4 Feb 2021 20:42:42 +0100 Subject: [PATCH] Add kedro-mlflow to the list of community plugins (#113) --- docs/source/07_extend_kedro/04_plugins.md | 1 + 1 file changed, 1 insertion(+) diff --git a/docs/source/07_extend_kedro/04_plugins.md b/docs/source/07_extend_kedro/04_plugins.md index f87452c75a..8a9254d8af 100644 --- a/docs/source/07_extend_kedro/04_plugins.md +++ b/docs/source/07_extend_kedro/04_plugins.md @@ -149,3 +149,4 @@ When you are ready to submit your code: - [Kedro-Accelerator](https://github.com/deepyaman/kedro-accelerator), by [Deepyaman Datta](https://github.com/deepyaman), speeds up pipelines by parallelizing I/O in the background - [kedro-dataframe-dropin](https://github.com/mzjp2/kedro-dataframe-dropin), by [Zain Patel](https://github.com/mzjp2), lets you swap out pandas datasets for modin or RAPIDs equivalents for specialised use to speed up your workflows (e.g on GPUs) - [kedro-kubeflow](https://github.com/getindata/kedro-kubeflow), by [Mateusz Pytel](https://github.com/em-pe) and [Mariusz Strzelecki](https://github.com/szczeles), lets you run and schedule pipelines on Kubernetes clusters using [Kubeflow Pipelines](https://www.kubeflow.org/docs/pipelines/overview/pipelines-overview/) +- [kedro-mlflow](https://github.com/Galileo-Galilei/kedro-mlflow), by [Yolan Honoré-Rougé](https://github.com/galileo-galilei), [Kajetan Maurycy Olszewski](https://github.com/kaemo), and [Takieddine Kadiri](https://github.com/takikadiri) facilitates [Mlflow](https://www.mlflow.org/) integration inside Kedro projects while enforcing [Kedro's principles](https://kedro.readthedocs.io/en/stable/12_faq/01_faq.html?highlight=principles#what-is-the-philosophy-behind-kedro). Its main features are modular configuration, automatic parameters tracking, datasets versioning, Kedro pipelines packaging and serving and automatic synchronization between training and inference pipelines for high reproducibility of machine learning experiments and ease of deployment. A tutorial is provided in the [kedro-mlflow-tutorial repo](https://github.com/Galileo-Galilei/kedro-mlflow-tutorial).