Update README.md

README.md

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-# Package (7)
-
-![branch build status](https://github.com/octo-technology/Formation-MLOps-1/actions/workflows/validation_ci.yml/badge.svg?branch=7_package)
-
-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
-- Your functions are tested
-- Your package have a documentation
-- Your code is packaged and installable
-
-This step is to go further, it is optional.
-
-What is the goal ?
--------------------
-The goal of this step is to experiment with pickle and create a predict notebook.
-
-You should :
-- Make a class for your processing steps
-- Save your model and processing class using pickle
-- Create a new notebook
-- Load data and import all needed code to process it
-- Load model using pickle
-- Make a prediction
-- Complete the Dockerfile
-- Build & run your docker image.
-
-- Bonus: Add api test.
-
-How to run the docker container ?
----------------------------------
-
-```
-docker build -t mlops-1 .
-docker run -p 80:80 mlops-1
-```
-
-The API is exposed and can be reached at 0.0.0.0:80
-The doc is available [here](http://127.0.0.1/docs), generated by swagger it can also be used to interact with the api.
-
-[Call example](http://127.0.0.1/predict/2/Nasser,%20Mrs.%20Nicholas%20(Adele%20Achem)/female/?&age=14.0&sibSp=1&parch=0&ticket=237736&fare=30.0708&embarked=C) that should return the following message:
-```
-input_proba:
-0	0.1997672538
-1	0.8002327462
-```
-
-When I'm done ?
----------------
-This is the end of this practical work.
-
-Now you know how to go from an 'ugly' notebook to a documented, tested package.
-
-To see a correction what you should have achieved you can check out branch `8_predict_notebook`
+# Formation MLOps 1 : Industrialisation de la Data Science
+
+Pour suivre ce TP, nous allons utiliser les GitHub pages suivantes : 
+
+[TP 0 Installation de l'environnement](https://octo-technology.github.io/Formation-MLOps-1/tp0#0)
+
+[TP 1 Nettoyer le notebook](https://octo-technology.github.io/Formation-MLOps-1/tp1#0)
+
+[TP 2 Écrire des tests unitaires](https://octo-technology.github.io/Formation-MLOps-1/tp2#0)
+
+[TP 3 Documenter avec Sphinx](https://octo-technology.github.io/Formation-MLOps-1/tp3#0)
+
+[TP 4 Écrire un script de CI](https://octo-technology.github.io/Formation-MLOps-1/tp4#0)
+
+[TP 5 Créer un package python](https://octo-technology.github.io/Formation-MLOps-1/tp5#0)
+
+[TP 6 Créer une API, et la conteneuriser](https://octo-technology.github.io/Formation-MLOps-1/tp6#0)
+