📝 Fix broken links to Kedro documentation

README.md

@@ -21,7 +21,7 @@
 
 # What is kedro-mlflow?
 
-``kedro-mlflow`` is a [kedro-plugin](https://kedro.readthedocs.io/en/stable/extend_kedro/plugins.html) for lightweight and portable integration of [mlflow](https://mlflow.org/docs/latest/index.html) capabilities inside [kedro](https://kedro.readthedocs.io/en/stable/index.html) projects. It enforces [``Kedro`` principles](https://kedro.readthedocs.io/en/stable/12_faq/01_faq.html?highlight=principles#what-is-the-philosophy-behind-kedro) to make mlflow usage as production ready as possible. Its core functionalities are :
+``kedro-mlflow`` is a [kedro-plugin](https://kedro.readthedocs.io/en/stable/extend_kedro/plugins.html) for lightweight and portable integration of [mlflow](https://mlflow.org/docs/latest/index.html) capabilities inside [kedro](https://kedro.readthedocs.io/en/stable/index.html) projects. It enforces [``Kedro`` principles](https://kedro.org/blog/development-principles-for-opinionated-teams) to make mlflow usage as production ready as possible. Its core functionalities are :
 
 - **versioning**: `kedro-mlflow` intends to enhance reproducibility for machine learning experimentation. With `kedro-mlflow` installed, you can effortlessly register your parameters or your datasets with minimal configuration in a kedro run. Later, you will be able to browse your runs in the mlflow UI, and retrieve the runs you want. This is directly linked to [Mlflow Tracking](https://www.mlflow.org/docs/latest/tracking.html).
 - **model packaging**: ``kedro-mlflow`` intends to be be an agnostic machine learning framework for people who want to write portable, production ready machine learning pipelines. It offers a convenient API to convert a Kedro pipeline to a ``model`` in the mlflow sense. Consequently, you can *API-fy* or serve your Kedro pipeline with one line of code, or share a model with without worrying of the preprocessing to be made for further use. This is directly linked to [Mlflow Models](https://www.mlflow.org/docs/latest/models.html).

docs/source/01_introduction/01_introduction.md

@@ -4,7 +4,7 @@
 
 ``Kedro`` is a python package which facilitates the prototyping of data pipelines. It aims at enforcing software engineering best practices (separation between I/O and compute, abstraction, templating...). It is specifically useful for machine learning projects since it provides within the same interface interactive objects for the exploration phase, and *Command Line Interface* (CLI) and configuration files for the production phase. This makes the transition from exploration to production as smooth as possible.
 
-For more details, see [Kedro's official documentation](https://kedro.readthedocs.io/en/stable/01_introduction/01_introduction.html).
+For more details, see [Kedro's official documentation](https://docs.kedro.org/en/stable/introduction/index.html).
 
 ## What is ``Mlflow``?
 

docs/source/03_getting_started/01_example_project.md

@@ -12,14 +12,20 @@ pip install kedro-mlflow==0.12.1
 
 ## Install the toy project
 
-For this end to end example, we will use the [kedro starter](https://docs.kedro.org/en/stable/starters/starters.html#official-kedro-starters) with the [iris dataset](https://github.com/quantumblacklabs/kedro-starter-pandas-iris).
+For this end to end example, we will use the [kedro starter](https://docs.kedro.org/en/stable/starters/starters.html#official-kedro-starters) with the [iris dataset](https://github.com/kedro-org/kedro-starters).
 
 We use this project because:
 
 - it covers most of the common use cases
 - it is compatible with older version of ``Kedro`` so newcomers are used to it
 - it is maintained by ``Kedro`` maintainers and therefore enforces some best practices.
 
+### Installation with ``kedro>=0.19.0``
+
+```{warning}
+For ``kedro>=0.19.0``, ``pandas-iris`` starter has been removed. It is recommended to install [``spaceflights-pandas`` starter instead](https://github.com/kedro-org/kedro-starters/tree/main/spaceflights-pandas).
+```
+
 ### Installation with ``kedro>=0.16.3``
 
 The default starter is now called "pandas-iris". In a new console, enter:

docs/source/04_experimentation_tracking/02_version_parameters.md

@@ -22,4 +22,4 @@ The parameters are registered node by node (and not in a single batch at the beg
 
 ### How are parameters detected by the plugin?
 
-The hook **detects parameters through their prefix ``params:`` or the value ``parameters``**. These are the [reserved keywords used by Kedro to define parameters](https://kedro.readthedocs.io/en/stable/03_tutorial/04_create_pipelines.html?highlight=params%3A#working-with-multiple-pipelines) in the ``pipeline.py`` file(s).
+The hook **detects parameters through their prefix ``params:`` or the value ``parameters``**. These are the [reserved keywords used by Kedro to define parameters](https://docs.kedro.org/en/stable/configuration/parameters.html#how-to-use-parameters) in the ``pipeline.py`` file(s).