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-# Extract code into source (3)
+# Formation MLOps 1 : Industrialisation de la Data Science
 
-![branch build status](https://github.com/octo-technology/Formation-MLOps-1/actions/workflows/ci.yml/badge.svg?branch=3_extract_code_into_source)
+Pour suivre ce TP, nous allons utiliser les GitHub pages suivantes : 
 
-What is this?
--------------
-At this step :
-- Your notebook is clean and running
-- You have a few documented functions
-- Your functions are in a specific `.py` file
+[TP 0 Installation de l'environnement](https://octo-technology.github.io/Formation-MLOps-1/tp0#0)
 
-What is the goal ?
--------------------
-The goal at this step is to test your functions.
+[TP 1 Nettoyer le notebook](https://octo-technology.github.io/Formation-MLOps-1/tp1#0)
 
-Following instructor demonstration you will create a few unit tests using
-`unittest` or `pytest` frame work.
+[TP 2 Écrire des tests unitaires](https://octo-technology.github.io/Formation-MLOps-1/tp2#0)
 
-Installation
-------------
-Install the required package in the proper environment.
+[TP 3 Documenter avec Sphinx](https://octo-technology.github.io/Formation-MLOps-1/tp3#0)
 
-```shell
-pip install -r requirements_test.txt
-```
+[TP 4 Écrire un script de CI](https://octo-technology.github.io/Formation-MLOps-1/tp4#0)
 
-When I'm done ?
----------------
-When you are done, please wait for the rest of the group.
+[TP 5 Créer un package python](https://octo-technology.github.io/Formation-MLOps-1/tp5#0)
 
-For the next step of the practical work, you can either
-keep on working on the code as it is, or checkout branch `4_create_unit_tests`
+[TP 6 Créer une API, et la conteneuriser](https://octo-technology.github.io/Formation-MLOps-1/tp6#0)
 
-To checkout branch `4_create_unit_tests` you need to either commit
-or stash your changes :
-```
-git stash
-git checkout 4_create_unit_tests
-```
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