Tech 5. May 18th 2018. Data Driven Innovation 2018. Engineering Department, University of Roma Tre

1. Nome Speaker
@twitter
Cloud Computing: New era in ML
Gianluigi Mucciolo
/mucciolo91

3. Raph C. Merkle Raymond Kurzweil PaulReber

4. The Machine Learning Process
Business
Problem
Re-training
Predictions
No Yes
DataAugmentation
Feature
Augmentation
Are Business
Goals met?

5. Knowledge Time Computation Money

8. AI Infrastructure

9. AI Frameworks
AWS Deep Learning AMI
• Easy-to-launch tutorials
• Hassle-free setup and configuration
• Pay only for what you use
• Accelerate your model training and deployment
• Support for popular deep learning frameworks

10. AI Services

12. Business Problem
Re-training
Integration: Data Architecture
Predictions
No Yes
DataAugmentation
Feature
Augmentation
Are Business
Goals met?

13. Business Problem
Re-training
Modelling: The Data Science
Predictions
No Yes
DataAugmentation
Feature
Augmentation
Are Business
Goals met?

14. Business Problem
Re-training
Production: The DevOps
Predictions
No Yes
DataAugmentation
Feature
Augmentation
Are Business
Goals met?

15. Amazon SageMaker is a fully managed service that enable developers
and data scientist to quickly and easilybuild, train anddeploymachine
learning models at any scale..
..from idea to production.

16. Amazon SageMaker Components

17. • Recommendations
• Personalization
• Fraud Detection
• Forecasting
• Image Classification
• Churn Prediction
• Marketing Email
• Campaign Targeting
• Log processing
• Anomaly detection
• Speech to Text
• More…
“Just add data”

19. Fully Managed
Secured

20. 4
ML Hosting Service
Different Versions of
the same inference
code in ECR. Prod is
the master container,
it will serve over 50%
of requests.
Amazon ECR
Model Artifacts

21. 4
ML Hosting Service
Amazon ECR
Model Artifacts
ModelName: prod
Create a Model
Different Versions of
the same inference
code in ECR. Prod is
the master container,
it will serve over 50%
of requests.

22. 4
ML Hosting Service
Amazon ECR
Model Artifacts
Model Versions
Create a versions of
a Model
Different Versions of
the same inference
code in ECR. Prod is
the master container,
it will serve over 50%
of requests.

23. 4
ML Hosting Service
InstanceType: c3.4xlarge
MinInstanceCount: 5
MaxInstanceCount: 20
ModelNmae: prod
VariantName: prodPrimary
VariantWeight: 50
ProductionVariant
Amazon ECR
Model Artifacts
30 50
10 10
Model Versions
Create weighted
ProductionVariants
Different Versions of
the same inference
code in ECR. Prod is
the master container,
it will serve over 50%
of requests.

24. 4
ML Hosting Service
InstanceType: c3.4xlarge
MinInstanceCount: 5
MaxInstanceCount: 20
ModelName: prod
VariantName: prodPrimary
VariantWeight: 50
ProductionVariant
Amazon ECR
Model Artifacts
Endpoint Configuration
30 50
10 10
Model Versions
Create an
EndpointConfiguration
from one or many
ProductionVariants
Different Versions of
the same inference
code in ECR. Prod is
the master container,
it will serve over 50%
of requests.

25. 4
ML Hosting Service
InstanceType: c3.4xlarge
MinInstanceCount: 5
MaxInstanceCount: 20
ModelNmae: prod
VariantName: prodPrimary
VariantWeight: 50
ProductionVariant
Amazon ECR
Model Artifacts
Endpoint Configuration
Inference Endpoint
30 50
10 10
Model Versions
Create an Endpoint
from one
EndpointConfiguration
Different Versions of
the same inference
code in ECR. Prod is
the master container,
it will serve over 50%
of requests.

26. • Training Algorithm / inference code is
packaged in Docker Image on ECR
• SageMaker pulls training algorithm image
from ECR into Model raining Service
• Amazon SM downloads or streams the
training data and runs Training
• After training Amazon SM uploads model
artifacts to Amazon S3
• For Inference, Amazon SM pulls the model
artifacts and the inference image from ECR
into Model Hosting Service
• Amazon SM exposes an inference
endpoint for client application to send
prediction requests
• Ground truth data collected from the client
could be sent into training bucket to retrain
and update the model

27. 4

29. Demo
• Build Workflow
• Addestramento
• Deploy

30. www.xpeppers.com
/xpepperssrl@xpeppers