AWS Cloud Experience Centro América 20 November 2019

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#AWSCLOUDEXPERIENCE

2. Democratizing Artificial
Intelligence with AWS
María Gaska
AI/ML Solution Architect
Amazon Web Services

3. Machine Learning at Amazon: A long heritage
Voice driven
interactions
Fulfillment automation
& inventory management
Personalized
recommendations
Drones
Inventing entirely new
customer experiences

4. Our mission at AWS
Put machine learning in the
hands of every developer

5. More machine learning happens on AWS

6. The Amazon Machine Learning stack
A I S E R V I C E S
(App developers with
little knowledge of ML)
A m a z o n
R e k o g n i t i o n
I m a g e
A m a z o n
P o l l y
A m a z o n
T r a n s c r i b e
A m a z o n
T r a n s l a t e
A m a z o n
C o m p r e h e n dA m a z o n
L e x
A m a z o n
R e k o g n i t i o n
V i d e o
Vision Speech LanguageChatbots
A m a z o n
F o r e c a s t
Forecasting
A m a z o n
T e x t r a c t
A m a z o n
P e r s o n a l i z e
RecommendationsDocuments
M L S E R V I C E S
(ML developers
and data scientists)
A m a z o n
S a g e M a k e r
G r o u n d T r u t h A l g o r i t h m s
N o t e b o o k s
M a r k e t p l a c e
U n s u p e r v i s e d
L e a r n i n g
S u p e r v i s e d
L e a r n i n g
R e i n f o r c e m e n t
L e a r n i n g
O p t i m i z a t i o n
( N e o )
T r a i n i n g
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
(ML researchers
and academics)
Infrastructure
A m a z o n
E C 2 P 3
& P 3 D N
A m a z o n
E C 2 C 5
F P G A s A W S G r e e n g r a s s A m a z o n
E l a s t i c
I n f e r e n c e
A m a z o n
I n f e r e n t i a

7. The Amazon Machine Learning stack
M L S E R V I C E S
(ML developers
and data scientists)
A m a z o n
S a g e M a k e r
G r o u n d T r u t h A l g o r i t h m s
N o t e b o o k s
M a r k e t p l a c e
U n s u p e r v i s e d
L e a r n i n g
S u p e r v i s e d
L e a r n i n g
R e i n f o r c e m e n t
L e a r n i n g
O p t i m i z a t i o n
( N e o )
T r a i n i n g

8. Amazon SageMaker
Bringing machine learning to all developers
C o l l e c t a n d
p r e p a r e t r a i n i n g
d a t a
C h o o s e a n d
o p t i m i z e y o u r M L
a l g o r i t h m
S e t u p a n d
m a n a g e
e n v i r o n m e n t s f o r
t r a i n i n g
Train and tune model
(trial and error)
Deploy model
in production
S c a l e a n d m a n a g e
t h e p r o d u c t i o n
e n v i r o n m e n t

9. Pre-built
notebooks for
common problems
C o l l e c t a n d
p r e p a r e
t r a i n i n g d a t a
C h o o s e a n d
o p t i m i z e y o u r M L
a l g o r i t h m
S e t u p a n d
m a n a g e
e n v i r o n m e n t s f o r
t r a i n i n g
Train and tune model
(trial and error)
Deploy model
in production
S c a l e a n d m a n a g e
t h e p r o d u c t i o n
e n v i r o n m e n t
Amazon SageMaker
Bringing machine learning to all developers

10. Pre-built
notebooks for
common problems
C o l l e c t a n d
p r e p a r e
t r a i n i n g d a t a
Train and tune model
(trial and error)
Deploy model
in production
S c a l e a n d m a n a g e
t h e p r o d u c t i o n
e n v i r o n m e n t
B u i l t - i n , h i g h
p e r f o r m a n c e
a l g o r i t h m s
C h o o s e a n d
o p t i m i z e y o u r
M L a l g o r i t h m
K-Means Clustering
Principal Component Analysis
Neural Topic Modelling
Factorization Machines
Linear Learner (Regression)
BlazingText
Reinforcement learning
XGBoost
Topic Modeling (LDA)
Image Classification
Seq2Seq
Linear Learner (Classification)
DeepAR Forecasting
Amazon SageMaker
Bringing machine learning to all
developers

11. Pre-built
notebooks for
common problems
C o l l e c t a n d
p r e p a r e
t r a i n i n g d a t a
Train and tune model
(trial and error)
Deploy model
in production
S c a l e a n d m a n a g e
t h e p r o d u c t i o n
e n v i r o n m e n t
B u i l t - i n , h i g h
p e r f o r m a n c e
a l g o r i t h m s
C h o o s e a n d
o p t i m i z e y o u r
M L a l g o r i t h m
One-click
training
Set up and manage
environments
for training
Amazon SageMaker
Bringing machine learning to all developers

12. Pre-built
notebooks for
common problems
C o l l e c t a n d
p r e p a r e
t r a i n i n g d a t a
Deploy model
in production
S c a l e a n d m a n a g e
t h e p r o d u c t i o n
e n v i r o n m e n t
B u i l t - i n , h i g h
p e r f o r m a n c e
a l g o r i t h m s
C h o o s e a n d
o p t i m i z e y o u r
M L a l g o r i t h m
One-click
training
Set up and manage
environments
for training
O p t i m i z a t i o n
Amazon SageMaker
Bringing machine learning to all developers
Train and tune
model
(trial and error)

13. Pre-built
notebooks for
common problems
C o l l e c t a n d
p r e p a r e
t r a i n i n g d a t a
S c a l e a n d m a n a g e
t h e p r o d u c t i o n
e n v i r o n m e n t
B u i l t - i n , h i g h
p e r f o r m a n c e
a l g o r i t h m s
C h o o s e a n d
o p t i m i z e y o u r
M L a l g o r i t h m
One-click
training
Set up and manage
environments
for training
O p t i m i z a t i o n One-click
deployment
Deploy model
in production
Amazon SageMaker
Bringing machine learning to all developers
Train and tune
model
(trial and error)

14. Amazon SageMaker
Bringing machine learning to all developers
Pre-built
notebooks for
common problems
C o l l e c t a n d
p r e p a r e
t r a i n i n g d a t a
B u i l t - i n , h i g h
p e r f o r m a n c e
a l g o r i t h m s
C h o o s e a n d
o p t i m i z e y o u r
M L a l g o r i t h m
One-click
training
Set up and manage
environments
for training
O p t i m i z a t i o n
F u l l y m a n a g e d w i t h
a u t o - s c a l i n g , h e a l t h
c h e c k s , a u t o m a t i c
h a n d l i n g o f n o d e
f a i l u r e s , a n d s e c u r i t y
c h e c k s
S c a l e a n d
m a n a g e t h e
p r o d u c t i o n
e n v i r o n m e n t
Train and tune
model
(trial and error)
One-click
deployment
Deploy model
in production

15. More than ten thousand customers using Amazon SageMaker

16. The Amazon Machine Learning stack
A I S E R V I C E S
(App developers with
little knowledge of ML)
A m a z o n
R e k o g n i t i o n
I m a g e
A m a z o n
P o l l y
A m a z o n
T r a n s c r i b e
A m a z o n
T r a n s l a t e
A m a z o n
C o m p r e h e n dA m a z o n
L e x
A m a z o n
R e k o g n i t i o n
V i d e o
Vision Speech LanguageChatbots
A m a z o n
F o r e c a s t
Forecasting
A m a z o n
T e x t r a c t
A m a z o n
P e r s o n a l i z e
RecommendationsDocuments
M L S E R V I C E S
(ML developers
and data scientists)
A m a z o n
S a g e M a k e r
G r o u n d T r u t h A l g o r i t h m s
N o t e b o o k s
M a r k e t p l a c e
U n s u p e r v i s e d
L e a r n i n g
S u p e r v i s e d
L e a r n i n g
R e i n f o r c e m e n t
L e a r n i n g
O p t i m i z a t i o n
( N e o )
T r a i n i n g
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
(ML researchers
and academics)
Infrastructure
A m a z o n
E C 2 P 3
& P 3 D N
A m a z o n
E C 2 C 5
F P G A s A W S G r e e n g r a s s A m a z o n
E l a s t i c
I n f e r e n c e
A m a z o n
I n f e r e n t i a

17. The Amazon Machine Learning stack
A I S E R V I C E S
(App developers with
little knowledge of ML)
A m a z o n
R e k o g n i t i o n
I m a g e
A m a z o n
P o l l y
A m a z o n
T r a n s c r i b e
A m a z o n
T r a n s l a t e
A m a z o n
C o m p r e h e n dA m a z o n
L e x
A m a z o n
R e k o g n i t i o n
V i d e o
Vision Speech LanguageChatbots
A m a z o n
F o r e c a s t
Forecasting
A m a z o n
T e x t r a c t
A m a z o n
P e r s o n a l i z e
RecommendationsDocuments

30. N o M L e x p e r i e n c e r e q u i r e d
NEW!
R e a l - t i m e p e r s o n a l i z a t i o n a n d
r e c o m m e n d a t i o n s e r v i c e , b a s e d o n
t h e s a m e t e c h n o l o g y u s e d a t
A m a z o n . c o m
A V A I L A B L E I N P R E V I E W T O D A Y
Amazon Personalize

31. Activity stream
from app
Views, signups,
conversion, etc.
Inventory
Articles, products,
videos, etc.
Demographics
(optional)
LOAD DATA
(EMR Cluster)
INSPECT
DATA
IDENTIFY
FEATURES
SELECT
ALGORITHMS
SELECT
HYPERPARAMETERS
TRAIN
MODELS
OPTIMIZE
MODELS
HOST
MODELS
BUILD FEATURE
STORE
CREATE
REAL-TIME
CACHES
Customized
personalization &
recommendation
API
F u lly ma n a g e d b y A ma zo n P e rs o nalize
Amazon Personalize
Amazon Personalize: machine learning personalization and recommendations
Age, location, etc.

32. NEW!
A c c u r a t e t i m e - s e r i e s f o r e c a s t i n g
s e r v i c e , b a s e d o n t h e s a m e
t e c h n o l o g y u s e d a t A m a z o n . c o m
A V A I L A B L E I N P R E V I E W T O D A Y
Amazon Forecast
N o M L e x p e r i e n c e r e q u i r e d

33. Historical data
Supply chain,
inventory, etc.
Customized
forecasting API
Inspect
data
Identify
features
Select
from 8
algorithms
Select
hyperparameters
Host
models
Load
data
Train
models
Optimize
models
Related “causal” data
Weather, special offers, product
details
F u l l y m a n a g e d b y A m a z o n
F o r e c a s t
Amazon Forecast
Amazon Forecast: machine learning time-series forecasting

34. Using Amazon Forecast for time-series forecasts
Any historical
time-series
Integrates with SAP and
Oracle Supply Chain
Custom forecasts
with 3 clicks
50% more
accurate
1/10th
the cost
Integrates with
Amazon Timestream
Retail demand Travel demand AWS usage
Revenue forecasts Web traffic Advertising demand
Generate forecasts for:

35. The Amazon Machine Learning stack
A I S E R V I C E S
(App developers with
little knowledge of ML)
A m a z o n
R e k o g n i t i o n
I m a g e
A m a z o n
P o l l y
A m a z o n
T r a n s c r i b e
A m a z o n
T r a n s l a t e
A m a z o n
C o m p r e h e n dA m a z o n
L e x
A m a z o n
R e k o g n i t i o n
V i d e o
Vision Speech LanguageChatbots
A m a z o n
F o r e c a s t
Forecasting
A m a z o n
T e x t r a c t
A m a z o n
P e r s o n a l i z e
RecommendationsDocuments
M L S E R V I C E S
(ML developers
and data scientists)
A m a z o n
S a g e M a k e r
G r o u n d T r u t h A l g o r i t h m s
N o t e b o o k s
M a r k e t p l a c e
U n s u p e r v i s e d
L e a r n i n g
S u p e r v i s e d
L e a r n i n g
R e i n f o r c e m e n t
L e a r n i n g
O p t i m i z a t i o n
( N e o )
T r a i n i n g
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
(ML researchers
and academics)
Infrastructure
A m a z o n
E C 2 P 3
& P 3 D N
A m a z o n
E C 2 C 5
F P G A s A W S G r e e n g r a s s A m a z o n
E l a s t i c
I n f e r e n c e
A m a z o n
I n f e r e n t i a

36. Zona Demo: Face detection architecture

37. Califica nuestra charla
#AWSCLOUDEXPERIENCE

38. ¡Thank You!