Cloud Computing and Virtualisation for Data Scientists

This repository contains materials for the introductory course about Cloud Computing (applied to GCP) and virtualization.

This course will be separated into three parts:

1. Intro to cloud computing and virtualization (3h)

Here we will talk a bit about the cloud, virtualization, etc... There will be a self-paced workshop about creating your first GCP instances, connecting to SSH etc.

Pedagogical key points :

• Intro to cloud computing
• Intro to Google Cloud Platform
• Creating our first GCP Instances
• Discovering SSH, creating ssh keys etc, connecting to ssh
• Discovering terminal multiplexing with tmux for detachable ssh sessions
• Interacting with google cloud storage
• Intro to Infrastructure as Code

2. Docker (4h)

We will discover docker using a small presentation and self-paced workshop We'll talk a bit about K8s

Pedagogical Key Points:

• Introduction to containers / Docker for Data Science
• Hands-on / self-paced workshop for Docker

3. Google Cloud Platform for Data Scientists (3h)

We will talk a bit more about using the cloud from a data science perspective (i.e. my job, and maybe your job !)

• A small presentation "Making the cloud part of the everyday job of a data scientist" + demo
• 1h of TP for deploying a "ML service" intro production using Cloud Run
• 1h of TP for discovering useful GCP services, launching managed jupyter notebooks etc.

Sources used to make this class

1 - Intro to cloud computing and virtualization https://yogeek.github.io/enseignement/Introduction_Virtualisation_CloudComputing/

https://github.com/yogeek/enseignement/blob/master/Introduction_GoogleCloudPlatform/GCP_Introduction_ISAE_2018.pdf?raw=true

2 - Docker https://slides.com/guillaumedupin/docker-2-2-7

https://github.com/docker-for-data-science/docker-for-data-science-tutorial

3 - GCP 4 Data Science https://github.com/fchouteau/tds-google-colab-demo