Speaker: Pedro Paez, Specialist Solutions Architect, AWS Amazon SageMaker is a fully managed platform that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. Amazon SageMaker takes away the heavy lifting of machine learning, thus removing the typical barriers associated with machine learning. In this session, we'll dive deep into the technical details of each of the modules of Amazon SageMaker to showcase the capabilities of the platform.

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Build, train and deploy Deep
Learning models on Amazon
Sagemaker
Pedro Paez
Specialist Solutions Architect
AWS

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Related breakouts
Accelerating ML adoption with our new AI services
Ben Snively
Building Business outcome with Machine Learning
on AWS
Barnam Bora
ML in the physical world with IoT
Timothee Cruse

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Machine Learning
Y=f(X)
X=input / Y=output
Given many (X,Y) => Infer f

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Discriminating between classes
Positive
Negative
Cats
Dogs
x
x
x
x

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Making non-linear linear

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Biological learning
Source: http://cs231n.github.io/neural-networks-1/

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Deep learning
Hidden layers
Input layer Output

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The “learning” in deep learning
0.4 0.3
0.2 0.9
...
...
backpropagation (gradient descent)
!" != !"
0.4 ± # 0.3 ± #
new
weights
new
weights
0
1
0
1
1
.
.
.
X
input
label
...
!"
...

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General Concepts
• A neural network with at least one hidden layer can approximate
any function
• The optimization is complicated and computationally very intensive
due to non-convexity of the optimization space

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© 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon Trademark
M L F R A M E W O R K S &
I N F R A S T R U C T U R E
The Amazon ML Stack: Broadest & Deepest Set of Capabilities
A I S E R V I C E S
R E K O G N I T I O N
I M A G E
P O L L Y T R A N S C R I B E T R A N S L A T E C O M P R E H E N D
C O M P R E H E N D
M E D I C A L
L E XR E K O G N I T I O N
V I D E O
Vision Speech Chatbots
A M A Z O N S A G E M A K E R
B U I L D T R A I N
F O R E C A S TT E X T R A C T P E R S O N A L I Z E
D E P L O Y
Pre-built algorithms & notebooks
Data labeling (G R O U N D T R U T H )
One-click model training & tuning
Optimization ( N E O )
One-click deployment & hosting
M L S E R V I C E S
F r a m e w o r k s I n t e r f a c e s I n f r a s t r u c t u r e
E C 2 P 3
& P 3 d n
E C 2 C 5 F P G A s G R E E N G R A S S E L A S T I C
I N F E R E N C E
Models without training data (REINFORCEMENT LEARNING)Algorithms & models ( A W S M A R K E T P L A C E )
Language Forecasting Recommendations
NEW NEWNEW
NEW
NEW
NEWNEW
NEW
NEW
RL Coach

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Amazon SageMaker:
Build, Train, and Deploy ML Models at Scale
1
2
3

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Amazon SageMaker:
Build, Train, and Deploy ML Models at Scale
RL Coach

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Amazon SageMaker:
Build, Train, and Deploy ML Models at Scale
G R E E N G R A S SE C 2 C 5

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Amazon SageMaker:
Build, Train, and Deploy ML Models at Scale

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Pre-configured environments to quickly build deep learning
applications

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AWS is framework agnostic
Choose from popular frameworks
Run them fully managed Or run them yourself

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The best place to run TensorFlow
Fastest time
for TensorFlow
65% 90%
30m 14m
• 85% of TensorFlow workloads in the
cloud runs on AWS (2018 Nucleus
report)
• Available w/ Amazon SageMaker and
the AWS Deep Learning AMIs

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AWS: Best platform to run PyTorch
Versatile PortableFlexible

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AWS is the best platform for Apache MXNet

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• Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial
Networks (UC Berkley):
• https://junyanz.github.io/CycleGAN/
https://github.com/dmlc/gluon-cv
https://github.com/pedrojpaez/summit_workshop

25. Thank you!
S U M M I T © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Pedro Paez
pppaez@amazon.com