Fix #34, #35, #36 - various docs typo spotted by @lawwu

CHANGELOG.md

@@ -7,6 +7,7 @@
 - Versioned datasets artifacts logging are handled correctly ([#41](https://github.com/Galileo-Galilei/kedro-mlflow/issues/41))
 - MlflowDataSet handles correctly datasets which are inherited from AbstractDataSet ([#45](https://github.com/Galileo-Galilei/kedro-mlflow/issues/45))
 - Change the test in `_generate_kedro_command` to accept both empty `Iterable`s(default in CLI mode) and `None` values (default in interactive mode) ([#50](https://github.com/Galileo-Galilei/kedro-mlflow/issues/50))
+- Fix various documentation typos ([#34](https://github.com/Galileo-Galilei/kedro-mlflow/pull/34), [#35](https://github.com/Galileo-Galilei/kedro-mlflow/pull/35), [#36](https://github.com/Galileo-Galilei/kedro-mlflow/pull/36) and more)
 
 ## [0.2.1] - 2018-08-06
 

docs/source/01_introduction/02_motivation.md

@@ -2,7 +2,7 @@
 ## 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](./01_introduction.md), ``Kedro``'s current versioning (as of version ``0.16.1``) 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 manipulation which do not involve machine learning, this plugin is not what ou are seeking for ;)
+If you do not use ``Kedro`` or if you do pure data manipulation 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
@@ -38,6 +38,6 @@ Above implementations have the advantage of being very straightforward and *mlfl
 |Logging metrics              |NA                      |NA                    |
 |Logging Pipeline as model    |``pipeline.py``         |``KedroPipelineModel`` and ``pipeline_ml``|
 
-In the current version (``kedro_mlflow=0.2.0``), kedro-mlflow do not provide interface to log metrics, set tags or log models outside a Kedro ``Pipeline``. These 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).
+In the current version (``kedro_mlflow=0.2.0``), kedro-mlflow does not provide interface to log metrics, set tags or log models outside a Kedro ``Pipeline``. These 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).
 
 _**Note:** the version ``0.2.0`` does not need any ``MLProject`` file to use mlflow inside your Kedro project. As seen in the [introduction](./01_introduction.md), this file overlaps with Kedro configuration files._

docs/source/02_hello_world_example/01_example_project.md

@@ -1,6 +1,6 @@
 # Example project
 ## Check your installation
-Create a conda envionment and ``kedro-mlflow`` (this will automatically install ``kedro>=0.16.0``).
+Create a conda environment and ``kedro-mlflow`` (this will automatically install ``kedro>=0.16.0``).
 
 ```console
 conda create -n km_example python=3.6.8 --yes