This document discusses using R and Amazon SageMaker for machine learning tasks. It introduces SageMaker and why R is useful. It then demonstrates building a recommendation system using SageMaker endpoints and APIs. Various techniques for model training and deployment are presented, including transfer learning, Docker containers, and Plumber for web APIs. Overall issues around ML engineering and next steps are discussed.

1. R Sagemaker
Yan So
AWSKRUG #datascience

2. • / Yan So
• ML ,
, 3D ,
,
• Digital TV Middleware
• #r #aws #datascience #ml #deeplearning #reproducibility #predictable #travel
#beer #routinelife #bouldering #nz
• E: 13imso@gmail.com
L: https://www.linkedin.com/in/yanso
S: @yanso

3. • Why R?
• Why AWS Sagemaker?
•

4. • Urbanbase AR
AR
https://j7e6c.app.goo.gl/J9vC

5. • , ,
• Demo

6. • Search
• Category
• Filter
• Recommendation
AR
=> !
=> Classification Problem ( )

7. Why deep learning?
• OpenCV
• Vision API Service
• Rule Based System
• Machine Learning
• Deep Learning
ModelData
Machine LearningData Model

8. Why NOT deep learning?
• OpenCV
• Vision API Service
• Rule Based System
• Machine Learning
• Deep Learning
/ ( )
1.
2. ( ..)
3.
4.
5. ML /
6. Feature Extraction
...

9. : https://www.slideshare.net/awskorea/amazon-sagemaker-awss-new-deep-learning-service-muhyun-kim

10. Model
•
•
#HTML #XML
R package: https://goo.gl/C7jR2e
•
#Selenium
https://www.seleniumhq.org
R package: https://goo.gl/wqWf26
• Discussion

11. Model
• 4 ( / / / )
• 1,000
•
( 12,000 )
•
AWS #S3
R Package: https://goo.gl/fVUx75

12. - R & Keras
• R & RStudio
( )
,
, => , , , Running other codes in R
• Keras
TensorFlow, CNTK, Theano high-
level neural network API
• R & Keras with GPU
R Keras https://keras.rstudio.com
Tensorflow https://tensorflow.rstudio.com

13. -
• R Studio Server
https://www.rstudio.com/products/rstudio/download-server
• Install keras package
https://keras.rstudio.com

14. -
It takes just a couple of minutes!

15. - Transfer Learning
• The central concept is to use a more complex but successful pre-trained
DNN model to 'transfer' its learning to your more simplified (or equally but
not more complex) problem.
• Successful models have
Tuning Parameters
Featurisers
=>
Reading: https://www.datasciencecentral.com/profiles/blogs/transfer-learning-deep-learning-for-everyone

16. - Transfer Learning
• Image Classification models
Xception
VGG16
VGG19
ResNet50
InceptionV3
Inception
ResNetV2
MobileNet
DenseNet
NASNet
• Pre-trained Model ?
=>
=> ImageNet Database ? (x)
https://github.com/fchollet/deep-learning-models/releases

17. - Transfer Learning
Total params: 23,587,712
Trainable params: 23,534,592
Non-trainable params: 53,120
Total params: 23,587,712
Trainable params: 0
Non-trainable params: 23,587,712
Total params: 24,113,284
Trainable params: 525,572
Non-trainable params: 23,587,712
Total params: 23,587,712
Trainable params: 14,976,000
Non-trainable params: 8,611,712

18. - Transfer Learning
• Do a lot of training and validation experiments as possible
as you could (including Hyper Parameter Optimisation)
=> Atomisation?
•
Training Data
Data Argumentation
TL Model
Optimiser
Learning Rate
Fine Tuning
acc: 0.9003
loss: 0.2697
val_acc: 0.8935
val_loss: 0.2901
Elapsed Time: 32,176s
Overfitting

19. • R vs Python
R language is not popular as much as other languages
• Plumber
R web API (flask in Python)
https://www.rplumber.io
• AWS Sagemaker
( ) Dockerised Image ,
AWS AWS
• What's in the dockerised image?
R+Keras , Model Training/Prediction , Plumber
web invoke , Model Image

20. • What's in the dockerised image?
R+Keras , Model Training/Prediction , Plumber
web invoke , Model Image

21. • Amazon Lab
https://github.com/awslabs/amazon-sagemaker-examples/tree/master/
advanced_functionality/r_bring_your_own

22. • GUI Endpoint
• Google Spreadsheet Model Release Note
Version | Endpoint Name | Model Name | Changes

23. •
Postman Endpoint
Endpoint
base64
Prediction Result

24. • ( )
1. AWS Sagemaker SDK
2. Postman Endpoint ( , Headers)
- R package: https://cloudyr.github.io (AWS in R)
https://github.com/cloudyr/aws.signature (AWS signature)
3. AWS Lambda wrapping API
• Discussion
Endpoint

25. Recommendation System

26. Recommendation System
• / / / 4
• ( : 90%)
• AR
•
• Demo
https://youtu.be/bjm4vebHjgA

27. Recommendation System
• AWS Architecture

28. Recommendation System
• Response Time
Multithreading Jobs
1.
2. AWS Rekognition
3. S3
Pre-Loading
plumber API running
( API
)
base64 ( s3 < base64 < )

29. : https://www.slideshare.net/awskorea/amazon-sagemaker-awss-new-deep-learning-service-muhyun-kim
Many things left to do
ML Engineering
Job
Reporting System
Visualisation
Endpoint health check
....
Recommendation System
Collaborative Filtering
Scoring
Response Time
Grouping & Segmentation
....
TODO

31. R Sagemaker
https://youtu.be/JlviFGa1Jh0
/
?
?
?
TRY IT
NO
YES
YES/NO