With the release of Google Cloud AutoML, Google Cloud Platform provides yet another out-of-the-box AI managed service. But this doesn’t mean that data scientists have no say in training and deploying customised machine learning models in the cloud. There are services, such as Cloud ML Engine, devoted to this specific goal. Let’s see how a data scientist can exploit GCP potential to expose its own model.

1. @Giuliabianchl
Predicting with Google
Cloud Platform
April 24, 2019 - Giulia Bianchi

2. @Giuliabianchl
@Giuliabianchl
giulbia
Data scientist
> DXD-WiMLDS <

3. @Giuliabianchl
End of 2017
Google AutoML announcement
GCP podcast 117

4. @Giuliabianchl
End of 2018
Google AI Hub announcement

5. @Giuliabianchl@Giuliabianchl
To access any of them you
need to have access to GCP
anyway...

6. @Giuliabianchl
● Set up a GCP account (gmail ID needed)
● Sign in Google Cloud Console and set up a project ($$$)
● Install Cloud SDK
Google Cloud Platform

7. @Giuliabianchl
Did you say Cloud?

8. @Giuliabianchl
● Cloud → managed services
● From "on-premises" to *aaS
○ IaaS
○ PaaS
○ SaaS
● Access to theoretically unlimited
resources
● Rapidity of provisioning
● Reliability
Cloud what ?! And why should I even care?

9. @Giuliabianchl
GCP AI services - non exhaustive!
SaaS
SaaS
SaaS
SaaS
PaaS
SaaS

10. @Giuliabianchl@Giuliabianchl
Cloud ML Engine… now known
as AI Platform

11. @Giuliabianchl
AI Platform Doc

12. @Giuliabianchl
Parenting 2.0
giulbia/baby_cry_detection
https://www.youtube.com/watch?v=N-LXrheCIKM

13. @Giuliabianchl
Training pipeline
Input
training
data
Feature
engineering
Train model
SVM
Model saved
in laptop

14. @Giuliabianchl
Prediction pipeline Preliminary step
Model
deployed in
RPi

15. @Giuliabianchl
Prediction pipeline
Input new
data
Feature
engineering
Predict!
Model
deployed in
RPi
Prediction
saved in RPi

16. @Giuliabianchl
Prediction pipeline
Input new
data
Feature
engineering
Predict!
Model
deployed in
RPi
Prediction
saved in RPi
Done
in
45
sec.

17. @Giuliabianchl
Parenting 3.0
giulbia/gcp-rpi
giulbia/baby_cry_rpi

18. @Giuliabianchl
Prediction pipeline Preliminary step
Model
deployed in
GCP

19. @Giuliabianchl
Prediction pipeline
Input new
data
Feature
engineering
Predict!
Model
deployed in
GCP
Prediction
saved in
GCP

20. @Giuliabianchl
Prediction pipeline
Input new
data
Feature
engineering
Predict!
Model
deployed in
GCP
Prediction
saved in
GCP
Done
in
5
sec.

21. @Giuliabianchl@Giuliabianchl
Some code

22. @Giuliabianchl
Cloud Function - main.py

23. @Giuliabianchl
Cloud Function - requirements.txt

24. @Giuliabianchl
Cloud Function - function deployment

25. @Giuliabianchl
Cloud Function - call to ML engine for prediction

26. @Giuliabianchl
Cloud Function - logs

27. @Giuliabianchl@Giuliabianchl
Take away

28. @Giuliabianchl
... has a huge potential for data scientists
... is not fully data scientist friendly yet
... it needs more documentation
... evolves super fast
Google Cloud Platform...

29. @Giuliabianchl
Thank you!

30. JUNE, 27th 2019 - PAN PIPER, PARIS
DATAXDAY.FR
> DXD-WiMLDS <