When should I use kedro-mlflow?

Basically, you should use kedro-mlflow in any Kedro project which involves machine learning / deep learning. As stated in the introduction, Kedro’s current versioning (as of version 0.16.6) is not sufficient for machine learning projects: it lacks a UI and a run management system. Besides, the KedroPipelineModel ability to serve a kedro pipeline as an API or a batch in one line of code is a great addition for collaboration and transition to production.

If you do not use Kedro or if you do pure data processing which do not involve machine learning, this plugin is not what you are seeking for ;)

Why should I use kedro-mlflow?

Benchmark of existing solutions

This paragraph gives a (quick) overview of existing solutions for mlflow integration inside Kedro projects.

Mlflow is very simple to add to any existing code. It is a 2-step process:

  • add log_{XXX} (either param, artifact, metric or model) functions where they are needed inside the code

  • add a MLProject at the root of the project to enable CLI execution. This file must contain all the possible execution steps (like the / in a kedro project).

Including mlflow inside a kedro project is consequently very easy: the logging functions can be added in the code, and the MLProject is very simple and is composed almost only of the kedro run command. You can find examples of such implementations:

  • the medium paper by QuantumBlack employees.

  • the associated github repo

  • other examples can be found on Github, but AFAIK all of them follow the very same principles.

Enforcing Kedro principles

Above implementations have the advantage of being very straightforward and mlflow compliant, but they break several Kedro principles:

  • the MLFLOW_TRACKING_URI which registers the database where runs are logged is declared inside the code instead of a configuration file, which hinders portability across environments and makes transition to production more difficult

  • the logging of different elements can be put in many places in the Kedro template (in the code of any function involved in a node, in a Hook, in the ProjectContext, in a transformer…). This is not compliant with the Kedro template where any object has a dedicated location. We want to avoid the logging to occur anywhere because:

    • it is very error-prone (one can forget to log one parameter)

    • it is hard to modify (if you want to remove / add / modify an mlflow action you have to find it in the code)

    • it prevents reuse (re-usable function must not contain mlflow specific code unrelated to their functional specificities, only their execution must be tracked).

kedro-mlflow enforces these best practices while implementing a clear interface for each mlflow action in Kedro template. Below chart maps the mlflow action to perform with the Python API provided by kedro-mlflow and the location in Kedro template where the action should be performed.

Mlflow action Template file Python API
Set up configuration mlflow.yml MlflowHook
Logging parameters mlflow.yml MlflowHook
Logging artifacts catalog.yml MlflowArtifactDataSet
Logging models catalog.yml MlflowModelLoggerDataSet and MlflowModelSaverDataSet
Logging metrics catalog.yml MlflowMetricsDataSet
Logging Pipeline as model KedroPipelineModel and pipeline_ml_factory

kedro-mlflow does not currently provide interface to set tags outside a Kedro Pipeline. Some of above decisions are subject to debate and design decisions (for instance, metrics are often updated in a loop during each epoch / training iteration and it does not always make sense to register the metric between computation steps, e.g. as a an I/O operation after a node run).


You do not need any MLProject file to use mlflow inside your Kedro project. As seen in the introduction, this file overlaps with Kedro configuration files.