The document discusses various aspects of machine learning and deep learning using Google CloudML, focusing on feature extraction, model training, and deployment. It highlights benefits like pre-trained models, transfer learning techniques, and the use of cloud resources for scalable computing. Additionally, it covers exporting models, A/B testing, and infrastructure configurations for optimal performance.

1. Tensor in the Sky with CloudML
Tensor in the Sky with CloudML
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2. Sylvain Lequeux
@slequeux
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3. WHY ?
Deep Learning
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4. Classic Machine Learning
Question Feature Extraction Algorithm Response
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5. Deep Learning
Question Algorithm Response
Feature Extraction
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6. Neural Networks
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7. Artificial Neural Networks
X
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8. Artificial Neural Networks
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9. WHY ?
Cloud
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10. From IaaS to SaaS
Infrastructure
as
a
Service
Platform
as
a
Service
Software
as
a
Service
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11. Google CloudML
SaaS
Train
Predict
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12. CloudML
Cloud Storage
Compute
Training Job
Model 1
Version 1
Version 2
Compute VM
Folder
version 1
Folder
version 2
Google CloudML
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13. WHY ?
Use Case
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16. Local Mode
At least, it’ll work
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Built-in models
There are pre-trained models
▼ Saves time for training
Param count approximation
▼ InceptionV3 : 23M
▼ VGG16 : 138M
Included into Tensorflow & Keras
Adapted architecture
▼ Conflutional
▼ MaxPooling
▼ Reccurrent
▼ ...
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19. Export model
Or How to Reuse
Weight stored in-memory
▼ Need to retrain each time you start
▼ Non-deterministic behaviour
Use Protobuf format to store model
▼ Google’s data exchange format
▼ Stores weights and architecture
Model Directory
saved_model.pb
variables directory
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20. Export model
Prediction
Base 64
encode
[[[ 1.0, 1.2, 1.3 ], ...
data:image/j
peg;base64,/
9j/4AAQSkZ
JRgABA...
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22. Serving In the Cloud
No diamonds, sorry
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23. Serving
Prediction
Base 64
encode
[[[ 1.0, 1.2, 1.3 ], ...
data:image/j
peg;base64,/
9j/4AAQSkZ
JRgABA...
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25. Google functions
Proxify HTTP requests
Perform controls :
▼ Authentication
▼ Bandwidth controls
▼ …
Transform data
▼ Input preprocessing
▼ Output formatting
Google functions
A/B test your models
Implements A/B testing logic inside this function
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26. Learning In the Cloud
How about Magritte ?
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28. Transfer learning [...] is a research
problem in machine learning that
focuses on storing knowledge gained
while solving one problem and applying
it to a different but related problem.
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29. Transfer learning
Retraining without retraining
First layers learn basic features
▼ Lines
▼ Colors
▼ ...
End layers learn complex features
▼ Objects
▼ Complex shapes
▼ …
Transfer learning is 2-steps :
▼ Freeze first layers
▼ Train new end layers
Layer 1
Layer 2
Layer N Layer N’
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30. Online training
Infinite power
▼ Define a setup.py file
▼ Define a config.yml file
▽ This could be done directly in step 3 by
passing args
▽ Defines CPU/GPU/TPU usage
▽ Defines runtime version
▽ ...
▼ Run it
▽ Using gcloud command line
▽ Use -- to separate gcloud args from your
job’s args
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32. So, what ?
Conclusions : 2 for 1
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37. Thank you
https://github.com/slequeux/xke-cloudml
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