This document provides an overview of using Python for development on Google Cloud. It discusses why Python is preferred for machine learning and outlines various Google Cloud products that can be used to deploy Python applications, including Cloud Run for containerized apps, Google Kubernetes Engine (GKE) for managing container clusters, and Anthos for hybrid/multi-cloud environments. It also provides examples of companies using these Google Cloud products with Python and includes links to YouTube channels and a QwikLabs lab for hands-on learning.

2. Python Development Environment
Setting up Python and run an example
Basheer Arab
AI Student at IAU

4. • Python on Google Cloud
• Python features and fields
• Why Python is preferred for Machine Learning ?
• QwikLabs – Dataflow : Python
• Kahoot
Outline

5. Get Familiar with Python
Our YouTube channel :
https://www.youtube.com/channel/UCXtnjxxrjBAsLB6URpHF0cA/videos
Part 1 Part 2

6. • Why do we need Python Development Environment
on Cloud?
• Why Python?
Questions you might ask

8. Google Cloud has the tools Python developers need to be successful
building cloud-native applications. Build your apps quicker with SDKs and
in-IDE assistance and then scale as big, or small, as you need on Cloud
Run, GKE, or Anthos.
Python on Google Cloud

9. ● Libraries optimized for Python ● Deep IDE integrations
● Find, diagnose, and fix complex
issues
● Run workloads anywhere
● Managed JupyterLab notebooks
Python on Google Cloud

10. Fully managed compute platform for deploying and scaling
containerized applications quickly and securely
Cloud Run
Cloud Run makes container deployment even easier. It’s
particularly good for:
- Developing software in cloud applications
- Delivering web apps, APIs, background jobs

12. Veolia uses Could Run to
remove the barriers of managed
platforms
Cloud Run allows Airbus to
process and serve large amounts
of imagery data stored in Google
Cloud Storage
Mailchimp triggers autoscaling and
deploys containerized software
with low complexity using Cloud Run
MediaMarktSaturn improved their
time to market by 8x with Cloud Run.

13. Secured and managed Kubernetes service with four-way auto
scaling and multi-cluster support.
Google Kubernetes Engine
GKE cluster control planes are automatically upgraded to run
new versions of Kubernetes as those versions become stable, so
you can take advantage of newer features from the open source
Kubernetes project.

14. Pizza Hut transforms its
ecommerce infrastructure and
speeds response time.
Alpha Vertex offers advanced
financial analytics with the highly
efficient GKE.
Mailchimp triggers autoscaling and
deploys containerized software
with low complexity using Cloud Run
Tokopedia scales to accommodate
major shopping events seamlessly
with GKE.

15. Google’s Anthos software promises a single, consistent way of
managing Kubernetes workloads across on-prem and public
cloud environments
Anthos

16. Kaeser Kompressoren
accelerates app modernization
with Anthos
HSBC used the Anthos-managed
hybrid-cloud environment to
reduce big data analytics
complexity and cost
KeyBank uses Anthos to develop personalized banking solutions for its
customers

20. Python for Machine Learning?
Python combines remarkable power with very clear
syntax. It has modules, classes, exceptions, very
high-level dynamic data types, and dynamic typing.
There are interfaces to many system calls and
libraries, as well as to various windowing systems.
Python is also usable as an extension language for
applications written in other languages that need
easy-to-use scripting or automation interfaces.

21. Lab Time!

22. QWIKLABS
Lab Link :
https://www.qwiklabs.com/focuses/1100?parent=catalog
Free Month Subscription :
http://bit.ly/dsc-2020-oct

24. @soil_sa
soil_sa
@IAU_CCSIT
IAU_CCSIT

25. DSC
@DSC_IAU
Basheer Arab
@Basheer_17