AWS Innovate 2020 - Cómo crear aplicaciones inteligentes con inteligencia artificial en AWS - Edzon Sanchez

2. © 2020, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Cómo crear aplicaciones inteligentes con
inteligencia artificial en AWS
Edzon Sánchez
Machine Learning Specialist

3. © 2020, Amazon Web Services, Inc. or its affiliates. All rights reserved.
VISIÓN SPEECH TEXTO BÚSQUEDAS CHATBOTS PERSONALIZACIÓN PRONÓSTICOS FRAUDES DESARROLLO
CENTROS DE
CONTACTO
Stackde servicios deAI/MachineLearning
SERVICIOS DE INTELIGENCIA ARTIFICIAL
Amazon
Rekognition
Amazon
Polly
Amazon
Transcribe
+Medical
Amazon
Comprehend
+Medical
Amazon
Translate
Amazon
Lex
Amazon
Personalize
Amazon
Forecast
Amazon
Fraud Detector
Amazon
CodeGuru
Amazon
Textract
Amazon
Kendra
Contact Lens
For Amazon
Connect
¡Nuevo! ¡Nuevo! ¡Nuevo! ¡Nuevo!

4. © 2020, Amazon Web Services, Inc. or its affiliates. All rights reserved.
VISIÓN SPEECH TEXTO BÚSQUEDAS CHATBOTS PERSONALIZACIÓN PRONÓSTICOS FRAUDES DESARROLLO
CENTROS DE
CONTACTO
Stackde servicios deAI/MachineLearning
SERVICIOS DE INTELIGENCIA
ARTIFICIAL
Amazon
Rekognition
Amazon
Polly
Amazon
Transcribe
+Medical
Amazon
Comprehend
+Medical
Amazon
Translate
Amazon
Lex
Amazon
Personalize
Amazon
Forecast
Amazon
Fraud Detector
Amazon
CodeGuru
Amazon
Textract
Amazon
Kendra
Contact Lens
For Amazon
Connect
¡Nuevo!
¡Nuevo! ¡Nuevo! ¡Nuevo! ¡Nuevo!
Ground
Truth
Augmented
AI
ML
Marketplace
Neo
Algoritmos
incluidos
Notebooks Experiments
Entrenamiento y
optimización de
modelos
Debugger Autopilot
Hosting de
modelos
Model Monitor
SERVICIOS DE MACHINE LEARNING
SageMaker Studio IDE ¡Nuevo!
¡Nuevo! ¡Nuevo! ¡Nuevo! ¡Nuevo! ¡Nuevo!
Amazon SageMaker

5. © 2020, Amazon Web Services, Inc. or its affiliates. All rights reserved.
VISIÓN SPEECH TEXTO BÚSQUEDAS CHATBOTS PERSONALIZACIÓN PRONÓSTICOS FRAUDES DESARROLLO
CENTROS DE
CONTACTO
Stackde servicios deAI/MachineLearning
SERVICIOS DE INTELIGENCIA ARTIFICIAL
Amazon
Rekognition
Amazon
Polly
Amazon
Transcribe
+Medical
Amazon
Comprehend
+Medical
Amazon
Translate
Amazon
Lex
Amazon
Personalize
Amazon
Forecast
Amazon
Fraud Detector
Amazon
CodeGuru
Amazon
Textract
Amazon
Kendra
Contact Lens
For Amazon
Connect
¡Nuevo!
¡Nuevo! ¡Nuevo! ¡Nuevo! ¡Nuevo!
Ground
Truth
Augmented
AI
ML
Marketplace
Neo
Algoritmos
incluidos
Notebooks Experiments
Entrenamiento y
optimización de
modelos
Debugger Autopilot
Hosting de
modelos
Model Monitor
Deep Learning
AMIs & Containers
GPUs &
CPUs
Elastic
Inference
Inferentia FPGA
SERVICIOS DE MACHINE LEARNING
ML FRAMEWORKS E INFRASTRUCTURA
SageMaker Studio IDE ¡Nuevo!
¡Nuevo! ¡Nuevo! ¡Nuevo! ¡Nuevo! ¡Nuevo!
Amazon SageMaker

6. © 2020, Amazon Web Services, Inc. or its affiliates. All rights reserved.

7. © 2020, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Amazon Rekognition
Reconocimiento de objetos,
escenas y actividades
Reconocimiento facial Análisis facial Rastreo de personas
Detección de contenido no
apropiado
Reconocimiento de
celebridades
Texto en imágenes
A n á l i s i s d e i m á g e n e s y v i d e o b a s a d o e n D e e p l e a r n i n g

8. © 2020, Amazon Web Services, Inc. or its affiliates. All rights reserved.
import boto3
region = boto3.Session().region_name
rekognition=boto3.client(service_name='rekognition', region_name=region)
bucket='bucket'
photo='photo.jpg'
response = rekognition.detect_labels(
Image={'S3Object':{'Bucket':bucket,'Name':photo}},
MaxLabels=10,
MinConfidence=0.8)
print('Detected labels for ' + photo)
for label in response['Labels']:
print ("Label: " + label['Name'])
print ("Confidence: " + str(label['Confidence']))
print ("Instances:")
for instance in label['Instances']:
print (" Bounding box")
print (" Top: " + str(instance['BoundingBox']['Top']))
print (" Left: " + str(instance['BoundingBox']['Left']))
print (" Width: " + str(instance['BoundingBox']['Width']))
print (" Height: " + str(instance['BoundingBox']['Height']))
print (" Confidence: " + str(instance['Confidence']))
Amazon Rekognition -Python

9. © 2020, Amazon Web Services, Inc. or its affiliates. All rights reserved.
import boto3
region = boto3.Session().region_name
rekognition=boto3.client(service_name='rekognition', region_name=region)
bucket='bucket'
photo='photo.jpg'
response = rekognition.detect_labels(
Image={'S3Object':{'Bucket':bucket,'Name':photo}},
MaxLabels=10,
MinConfidence=0.8)
print('Detected labels for ' + photo)
for label in response['Labels']:
print ("Label: " + label['Name'])
print ("Confidence: " + str(label['Confidence']))
print ("Instances:")
for instance in label['Instances']:
print (" Bounding box")
print (" Top: " + str(instance['BoundingBox']['Top']))
print (" Left: " + str(instance['BoundingBox']['Left']))
print (" Width: " + str(instance['BoundingBox']['Width']))
print (" Height: " + str(instance['BoundingBox']['Height']))
print (" Confidence: " + str(instance['Confidence']))
Amazon Rekognition -Python

10. © 2020, Amazon Web Services, Inc. or its affiliates. All rights reserved.
import boto3
region = boto3.Session().region_name
rekognition=boto3.client(service_name='rekognition', region_name=region)
bucket='bucket'
photo='photo.jpg'
response = rekognition.detect_labels(
Image={'S3Object':{'Bucket':bucket,'Name':photo}},
MaxLabels=10,
MinConfidence=0.8)
print('Detected labels for ' + photo)
for label in response['Labels']:
print ("Label: " + label['Name'])
print ("Confidence: " + str(label['Confidence']))
print ("Instances:")
for instance in label['Instances']:
print (" Bounding box")
print (" Top: " + str(instance['BoundingBox']['Top']))
print (" Left: " + str(instance['BoundingBox']['Left']))
print (" Width: " + str(instance['BoundingBox']['Width']))
print (" Height: " + str(instance['BoundingBox']['Height']))
print (" Confidence: " + str(instance['Confidence']))
Amazon Rekognition -Python

11. © 2020, Amazon Web Services, Inc. or its affiliates. All rights reserved.
import boto3
region = boto3.Session().region_name
rekognition=boto3.client(service_name='rekognition', region_name=region)
bucket='bucket'
photo='photo.jpg'
response = rekognition.detect_labels(
Image={'S3Object':{'Bucket':bucket,'Name':photo}},
MaxLabels=10,
MinConfidence=0.8)
print('Detected labels for ' + photo)
for label in response['Labels']:
print ("Label: " + label['Name'])
print ("Confidence: " + str(label['Confidence']))
print ("Instances:")
for instance in label['Instances']:
print (" Bounding box")
print (" Top: " + str(instance['BoundingBox']['Top']))
print (" Left: " + str(instance['BoundingBox']['Left']))
print (" Width: " + str(instance['BoundingBox']['Width']))
print (" Height: " + str(instance['BoundingBox']['Height']))
print (" Confidence: " + str(instance['Confidence']))
Amazon Rekognition -Python

12. © 2020, Amazon Web Services, Inc. or its affiliates. All rights reserved.
import boto3
region = boto3.Session().region_name
rekognition=boto3.client(service_name='rekognition', region_name=region)
bucket='bucket'
photo='photo.jpg'
response = rekognition.detect_labels(
Image={'S3Object':{'Bucket':bucket,'Name':photo}},
MaxLabels=10,
MinConfidence=0.8)
print('Detected labels for ' + photo)
for label in response['Labels']:
print ("Label: " + label['Name'])
print ("Confidence: " + str(label['Confidence']))
print ("Instances:")
for instance in label['Instances']:
print (" Bounding box")
print (" Top: " + str(instance['BoundingBox']['Top']))
print (" Left: " + str(instance['BoundingBox']['Left']))
print (" Width: " + str(instance['BoundingBox']['Width']))
print (" Height: " + str(instance['BoundingBox']['Height']))
print (" Confidence: " + str(instance['Confidence']))
Amazon Rekognition -Python

13. © 2020, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Amazon Rekognition -Pythonimport boto3
region = boto3.Session().region_name
rekognition=boto3.client(service_name='rekognition', region_name=region)
bucket='bucket'
photo='photo.jpg'
response = rekognition.detect_labels(
Image={'S3Object':{'Bucket':bucket,'Name':photo}},
MaxLabels=10,
MinConfidence=0.8)
print('Detected labels for ' + photo)
for label in response['Labels']:
print ("Label: " + label['Name'])
print ("Confidence: " + str(label['Confidence']))
print ("Instances:")
for instance in label['Instances']:
print (" Bounding box")
print (" Top: " + str(instance['BoundingBox']['Top']))
print (" Left: " + str(instance['BoundingBox']['Left']))
print (" Width: " + str(instance['BoundingBox']['Width']))
print (" Height: " + str(instance['BoundingBox']['Height']))
print (" Confidence: " + str(instance['Confidence']))

14. © 2020, Amazon Web Services, Inc. or its affiliates. All rights reserved.
AmazonTranscribe
C o n v e r s i ó n a u t o m á t i c a d e v o z e n t e x t o p r e c i s o y g r a m a t i c a l m e n t e c o r r e c t o
Soporte para
audio telefónico
Time stamps a
nivel de palabra
Puntuación y formato
automático
Identificación de
interlocutor
Vocabulario
personalizado
Multiples
idiomas

15. © 2020, Amazon Web Services, Inc. or its affiliates. All rights reserved.
AmazonTranslate
Tr a d u c c i ó n c o n f l u i d e z n a t u r a l
Reconocimiento
automático de idioma
Análisis en batchTraducción en
tiempo-real

16. © 2020, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Amazon Polly
C o n v i e r t e t e x t o e n v o z q u e p a r e c e n a t u r a l , u t i l i z a n d o d e e p l e a r n i n g
Amplia selección de
voces e idiomas
Voz sincronizable Fino control granular Reproducción
ilimitada

17. © 2020, Amazon Web Services, Inc. or its affiliates. All rights reserved.
AmazonComprehend
D e s c u b r e i n f o r m a c i ó n r e l e v a n t e y r e l a c i o n e s e n e l t e x t o
Amazon
Comprehend
Entidades
Frases clave
Idioma
Sentimiento
Modelado de tópico

18. © 2020, Amazon Web Services, Inc. or its affiliates. All rights reserved.
AmazonComprehend -Python
import boto3
import json
region = boto3.Session().region_name
comprehend = boto3.client(service_name='comprehend', region_name=region)
text = 'Amazon.com, Inc localizado en Seattle, WA fue fundado
el 5 de Julio de 1994 por Jeff Bezos'
lang = comprehend.detect_dominant_language(Text=text)
response = comprehend.detect_entities(Text=text, LanguageCode=lang)
print(json.dumps(response, sort_keys=True, indent=4))
response = comprehend.detect_key_phrases(Text=text, LanguageCode=lang)
print(json.dumps(response, sort_keys=True, indent=4))
response = comprehend.detect_sentiment(Text=text, LanguageCode=lang)
print(json.dumps(response, sort_keys=True, indent=4))

19. © 2020, Amazon Web Services, Inc. or its affiliates. All rights reserved.
AmazonComprehend -Python
import boto3
import json
region = boto3.Session().region_name
comprehend = boto3.client(service_name='comprehend', region_name=region)
text = 'Amazon.com, Inc localizado en Seattle, WA fue fundado
el 5 de Julio de 1994 por Jeff Bezos'
lang = comprehend.detect_dominant_language(Text=text)
response = comprehend.detect_entities(Text=text, LanguageCode=lang)
print(json.dumps(response, sort_keys=True, indent=4))
response = comprehend.detect_key_phrases(Text=text, LanguageCode=lang)
print(json.dumps(response, sort_keys=True, indent=4))
response = comprehend.detect_sentiment(Text=text, LanguageCode=lang)
print(json.dumps(response, sort_keys=True, indent=4))

20. © 2020, Amazon Web Services, Inc. or its affiliates. All rights reserved.
AmazonComprehend -Python
import boto3
import json
region = boto3.Session().region_name
comprehend = boto3.client(service_name='comprehend', region_name=region)
text = 'Amazon.com, Inc localizado en Seattle, WA fue fundado
el 5 de Julio de 1994 por Jeff Bezos'
lang = comprehend.detect_dominant_language(Text=text)
response = comprehend.detect_entities(Text=text, LanguageCode=lang)
print(json.dumps(response, sort_keys=True, indent=4))
response = comprehend.detect_key_phrases(Text=text, LanguageCode=lang)
print(json.dumps(response, sort_keys=True, indent=4))
response = comprehend.detect_sentiment(Text=text, LanguageCode=lang)
print(json.dumps(response, sort_keys=True, indent=4))

21. © 2020, Amazon Web Services, Inc. or its affiliates. All rights reserved.
AmazonComprehend -Python
import boto3
import json
region = boto3.Session().region_name
comprehend = boto3.client(service_name='comprehend', region_name=region)
text = 'Amazon.com, Inc localizado en Seattle, WA fue fundado
el 5 de Julio de 1994 por Jeff Bezos'
lang = comprehend.detect_dominant_language(Text=text)
response = comprehend.detect_entities(Text=text, LanguageCode=lang)
print(json.dumps(response, sort_keys=True, indent=4))
response = comprehend.detect_key_phrases(Text=text, LanguageCode=lang)
print(json.dumps(response, sort_keys=True, indent=4))
response = comprehend.detect_sentiment(Text=text, LanguageCode=lang)
print(json.dumps(response, sort_keys=True, indent=4))

22. © 2020, Amazon Web Services, Inc. or its affiliates. All rights reserved.
AmazonComprehend -Python
import boto3
import json
region = boto3.Session().region_name
comprehend = boto3.client(service_name='comprehend', region_name=region)
text = 'Amazon.com, Inc localizado en Seattle, WA fue fundado
el 5 de Julio de 1994 por Jeff Bezos'
lang = comprehend.detect_dominant_language(Text=text)
response = comprehend.detect_entities(Text=text, LanguageCode=lang)
print(json.dumps(response, sort_keys=True, indent=4))
response = comprehend.detect_key_phrases(Text=text, LanguageCode=lang)
print(json.dumps(response, sort_keys=True, indent=4))
response = comprehend.detect_sentiment(Text=text, LanguageCode=lang)
print(json.dumps(response, sort_keys=True, indent=4))

23. © 2020, Amazon Web Services, Inc. or its affiliates. All rights reserved.
AmazonComprehend -Python
import boto3
import json
region = boto3.Session().region_name
comprehend = boto3.client(service_name='comprehend', region_name=region)
text = 'Amazon.com, Inc localizado en Seattle, WA fue fundado
el 5 de Julio de 1994 por Jeff Bezos'
lang = comprehend.detect_dominant_language(Text=text)
response = comprehend.detect_entities(Text=text, LanguageCode=lang)
print(json.dumps(response, sort_keys=True, indent=4))
response = comprehend.detect_key_phrases(Text=text, LanguageCode=lang)
print(json.dumps(response, sort_keys=True, indent=4))
response = comprehend.detect_sentiment(Text=text, LanguageCode=lang)
print(json.dumps(response, sort_keys=True, indent=4))

24. © 2020, Amazon Web Services, Inc. or its affiliates. All rights reserved.
AmazonComprehend -Python
import boto3
import json
region = boto3.Session().region_name
comprehend = boto3.client(service_name='comprehend', region_name=region)
text = 'Amazon.com, Inc localizado en Seattle, WA fue fundado
el 5 de Julio de 1994 por Jeff Bezos'
lang = comprehend.detect_dominant_language(Text=text)
response = comprehend.detect_entities(Text=text, LanguageCode=lang)
print(json.dumps(response, sort_keys=True, indent=4))
response = comprehend.detect_key_phrases(Text=text, LanguageCode=lang)
print(json.dumps(response, sort_keys=True, indent=4))
response = comprehend.detect_sentiment(Text=text, LanguageCode=lang)
print(json.dumps(response, sort_keys=True, indent=4))

25. © 2020, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Amazon Lex
R e c o n o c i m i e n t o a u t o m á t i c o d e v o z p a r a c o n s t r u i r a p l i c a c i o n e s c o n v e r s a c i o n a l e s
Desarrollo
integrado a la
consola AWS
Invoca
funciones
Lambda
Conversaciones
de multiples pasos
Despliegue
con un clic
Conectores
empresariales
Totalmente
administrado

26. © 2020, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Elimina esfuerzo
manual
Disminuye los costos de
procesamiento de
documentos
P R I N C I PA L E S C A R A C T E R Í S T I C A S
Extrae datos de manera
rápida y exacta
Reconocimiento
Óptico de
Caracteres (OCR)
Detección de
pares llave-valor
Umbrales de confianza
ajustables
Detección de
tablas
Cuadros
delimitadores
No se require
experiencia en ML
E x t r a e t e x t o y d a t o s d e v i r t u a l m e n t e c u a l q u i e r d o c u m e n t o
AmazonTextract

27. © 2020, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Fácil de utilizar, desde
la consola y el API
Funciona con cualquier
serie de tiempo histórica
P R I N C I PA L E S C A R A C T E R Í S T I C A S
Pronósticos más exactos
que integran datos
externos
Considera múltiples
series de tiempo a la
vez
Machine
Learning
automático
Visualiza los
pronósticos en la
consola e importa a
aplicaciones de negocio
Evalúa la exactitud
del modelo a través
de la consola
Calendariza los
pronósticos y el re-
entrenamiento
Integra algoritmos
existentes de
Amazon SageMaker
Amazon Forecast
M e j o r a l a e x a c t i t u d d e l a s p r e d i c c i o n e s h a s t a u n 5 0 % a u n 1 / 1 0 d e l c o s t o

28. © 2020, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Tiempo-real Funciona con casi
cualquier product o
contenido
Responde a los cambios
de intención
Machine Learning
automático
Integra algoritmos existentes
de Amazon SageMaker
Entrega
recomendaciones de
alta calidad
Algoritmos de Deep
Learning
Fácil de utilizar
Amazon Personalize
M e j o r a l a e x p e r i e n c i a d e l o s c l i e n t e s c o n r e c o m e n d a c i o n e s y p e r s o n a l i z a c i ó n
P R I N C I PA L E S C A R A C T E R Í S T I C A S

29. © 2020, Amazon Web Services, Inc. or its affiliates. All rights reserved.

30. © 2020, Amazon Web Services, Inc. or its affiliates. All rights reserved.
AmazonSageMaker
L l e v a n d o M a c h i n e L e a r n i n g a t o d o s l o s d e s a r r o l l a d o r e s
Recolección y
preparación de los datos
de entrenamiento
Selección y
optimización del
algoritmo
Preparación y
administración de
ambientes para el
entrenamiento
Entrenamiento y
optimización del
modelo
Despliegue del
modelo en
producción
Administración y
escalamiento de
ambiente de
producción

31. © 2020, Amazon Web Services, Inc. or its affiliates. All rights reserved.
AmazonSageMaker
Recolección y
preparación de los datos
de entrenamiento
Selección y
optimización del
algoritmo
Preparación y
administración de
ambientes para el
entrenamiento
Entrenamiento y
optimización del
modelo
Despliegue del
modelo en
producción
Administración y
escalamiento de
ambiente de
producción
Notebooks pre-
construidos para
problemas comunes
L l e v a n d o M a c h i n e L e a r n i n g a t o d o s l o s d e s a r r o l l a d o r e s

32. © 2020, Amazon Web Services, Inc. or its affiliates. All rights reserved.
AmazonSageMaker
• 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
L l e v a n d o M a c h i n e L e a r n i n g a t o d o s l o s d e s a r r o l l a d o r e s
Recolección y
preparación de los datos
de entrenamiento
Selección y
optimización del
algoritmo
Notebooks pre-
construidos para
problemas comunes
Algoritmos con alto
desempeño incluidos

33. © 2020, Amazon Web Services, Inc. or its affiliates. All rights reserved.
AmazonSageMaker
Entrenamiento con
un solo clic
L l e v a n d o M a c h i n e L e a r n i n g a t o d o s l o s d e s a r r o l l a d o r e s
Recolección y
preparación de los datos
de entrenamiento
Selección y
optimización del
algoritmo
Preparación y
administración de
ambientes para el
entrenamiento
Entrenamiento y
optimización del
modelo
Despliegue del
modelo en
producción
Administración y
escalamiento de
ambiente de
producción
Notebooks pre-
construidos para
problemas comunes
Algoritmos con alto
desempeño incluidos

34. © 2020, Amazon Web Services, Inc. or its affiliates. All rights reserved.
AmazonSageMaker
Optimización
L l e v a n d o M a c h i n e L e a r n i n g a t o d o s l o s d e s a r r o l l a d o r e s
Recolección y
preparación de los datos
de entrenamiento
Selección y
optimización del
algoritmo
Preparación y
administración de
ambientes para el
entrenamiento
Entrenamiento y
optimización del
modelo
Despliegue del
modelo en
producción
Administración y
escalamiento de
ambiente de
producción
Notebooks pre-
construidos para
problemas comunes
Algoritmos con alto
desempeño incluidos
Entrenamiento con
un solo click

35. © 2020, Amazon Web Services, Inc. or its affiliates. All rights reserved.
AmazonSageMaker
Despliegue con
un clic
L l e v a n d o M a c h i n e L e a r n i n g a t o d o s l o s d e s a r r o l l a d o r e s
Recolección y
preparación de los datos
de entrenamiento
Selección y
optimización del
algoritmo
Preparación y
administración de
ambientes para el
entrenamiento
Entrenamiento y
optimización del
modelo
Despliegue del
modelo en
producción
Administración y
escalamiento de
ambiente de
producción
Notebooks pre-
construidos para
problemas comunes
Algoritmos con alto
desempeño incluidos
Entrenamiento con
un solo click Optimización

36. © 2020, Amazon Web Services, Inc. or its affiliates. All rights reserved.
AmazonSageMaker
Totalmente administrado
con auto-escalamiento,
verificación de estado,
reposición automática de
nodos fallidos y
verificaciones de seguridad
L l e v a n d o M a c h i n e L e a r n i n g a t o d o s l o s d e s a r r o l l a d o r e s
Recolección y
preparación de los datos
de entrenamiento
Selección y
optimización del
algoritmo
Preparación y
administración de
ambientes para el
entrenamiento
Entrenamiento y
optimización del
modelo
Despliegue del
modelo en
producción
Administración y
escalamiento de
ambiente de
producción
Notebooks pre-
construidos para
problemas comunes
Algoritmos con alto
desempeño incluidos
Entrenamiento con
un solo click Optimización
Despliegue con
un click

37. © 2020, Amazon Web Services, Inc. or its affiliates. All rights reserved.
AmazonSageMaker -XGBoost
import sagemaker
import boto3
from sagemaker import get_execution_role
from sagemaker.amazon.amazon_estimator import get_image_uri
sess = sagemaker.Session()
role = get_execution_role()
container = get_image_uri(boto3.Session().region_name, 'xgboost', '0.90-1')
xgboost = sagemaker.estimator.Estimator(container,
role,
train_instance_count=1,
train_instance_type='ml.c5.xlarge',
output_path='s3://bucket/path/output',
sagemaker_session=sess)
xgboost.set_hyperparameters(max_depth=6, eta=0.3, gamma=0, min_child_weight=1,
subsample=1, silent=0, objective="reg:linear", num_round=120)
xgboost.fit({'train':'s3://bucket/path/training/data',
'test': 's3://bucket/path/test/data'})

38. © 2020, Amazon Web Services, Inc. or its affiliates. All rights reserved.
AmazonSageMaker -XGBoost
import sagemaker
import boto3
from sagemaker import get_execution_role
from sagemaker.amazon.amazon_estimator import get_image_uri
sess = sagemaker.Session()
role = get_execution_role()
container = get_image_uri(boto3.Session().region_name, 'xgboost', '0.90-1')
xgboost = sagemaker.estimator.Estimator(container,
role,
train_instance_count=1,
train_instance_type='ml.c5.xlarge',
output_path='s3://bucket/path/output',
sagemaker_session=sess)
xgboost.set_hyperparameters(max_depth=6, eta=0.3, gamma=0, min_child_weight=1,
subsample=1, silent=0, objective="reg:linear", num_round=120)
xgboost.fit({'train':'s3://bucket/path/training/data',
'test': 's3://bucket/path/test/data'})

39. © 2020, Amazon Web Services, Inc. or its affiliates. All rights reserved.
AmazonSageMaker -XGBoost
import sagemaker
import boto3
from sagemaker import get_execution_role
from sagemaker.amazon.amazon_estimator import get_image_uri
sess = sagemaker.Session()
role = get_execution_role()
container = get_image_uri(boto3.Session().region_name, 'xgboost', '0.90-1')
xgboost = sagemaker.estimator.Estimator(container,
role,
train_instance_count=1,
train_instance_type='ml.c5.xlarge',
output_path='s3://bucket/path/output',
sagemaker_session=sess)
xgboost.set_hyperparameters(max_depth=6, eta=0.3, gamma=0, min_child_weight=1,
subsample=1, silent=0, objective="reg:linear", num_round=120)
xgboost.fit({'train':'s3://bucket/path/training/data',
'test': 's3://bucket/path/test/data'})

40. © 2020, Amazon Web Services, Inc. or its affiliates. All rights reserved.
AmazonSageMaker -XGBoost
import sagemaker
import boto3
from sagemaker import get_execution_role
from sagemaker.amazon.amazon_estimator import get_image_uri
sess = sagemaker.Session()
role = get_execution_role()
container = get_image_uri(boto3.Session().region_name, 'xgboost', '0.90-1')
xgboost = sagemaker.estimator.Estimator(container,
role,
train_instance_count=1,
train_instance_type='ml.c5.xlarge',
output_path='s3://bucket/path/output',
sagemaker_session=sess)
xgboost.set_hyperparameters(max_depth=6, eta=0.3, gamma=0, min_child_weight=1,
subsample=1, silent=0, objective="reg:linear", num_round=120)
xgboost.fit({'train':'s3://bucket/path/training/data',
'test': 's3://bucket/path/test/data'})

41. © 2020, Amazon Web Services, Inc. or its affiliates. All rights reserved.
AmazonSageMaker -XGBoost
import sagemaker
import boto3
from sagemaker import get_execution_role
from sagemaker.amazon.amazon_estimator import get_image_uri
sess = sagemaker.Session()
role = get_execution_role()
container = get_image_uri(boto3.Session().region_name, 'xgboost', '0.90-1')
xgboost = sagemaker.estimator.Estimator(container,
role,
train_instance_count=1,
train_instance_type='ml.c5.xlarge',
output_path='s3://bucket/path/output',
sagemaker_session=sess)
xgboost.set_hyperparameters(max_depth=6, eta=0.3, gamma=0, min_child_weight=1,
subsample=1, silent=0, objective="reg:linear", num_round=120)
xgboost.fit({'train':'s3://bucket/path/training/data',
'test': 's3://bucket/path/test/data'})

42. © 2020, Amazon Web Services, Inc. or its affiliates. All rights reserved.
AmazonSageMaker -XGBoost
import sagemaker
import boto3
from sagemaker import get_execution_role
from sagemaker.amazon.amazon_estimator import get_image_uri
sess = sagemaker.Session()
role = get_execution_role()
container = get_image_uri(boto3.Session().region_name, 'xgboost', '0.90-1')
xgboost = sagemaker.estimator.Estimator(container,
role,
train_instance_count=1,
train_instance_type='ml.c5.xlarge',
output_path='s3://bucket/path/output',
sagemaker_session=sess)
xgboost.set_hyperparameters(max_depth=6, eta=0.3, gamma=0, min_child_weight=1,
subsample=1, silent=0, objective="reg:linear", num_round=120)
xgboost.fit({'train':'s3://bucket/path/training/data',
'test': 's3://bucket/path/test/data'})

43. © 2020, Amazon Web Services, Inc. or its affiliates. All rights reserved.
AmazonSageMaker -XGBoost
import sagemaker
import boto3
from sagemaker import get_execution_role
from sagemaker.amazon.amazon_estimator import get_image_uri
sess = sagemaker.Session()
role = get_execution_role()
container = get_image_uri(boto3.Session().region_name, 'xgboost', '0.90-1')
xgboost = sagemaker.estimator.Estimator(container,
role,
train_instance_count=1,
train_instance_type='ml.c5.xlarge',
output_path='s3://bucket/path/output',
sagemaker_session=sess)
xgboost.set_hyperparameters(max_depth=6, eta=0.3, gamma=0, min_child_weight=1,
subsample=1, silent=0, objective="reg:linear", num_round=120)
xgboost.fit({'train':'s3://bucket/path/training/data',
'test': 's3://bucket/path/test/data'})

44. © 2020, Amazon Web Services, Inc. or its affiliates. All rights reserved.
AmazonSageMaker -XGBoost
xgboost_predictor = xgboost.deploy(initial_instance_count=1, instance_type='ml.m5.xlarge')
from sagemaker.predictor import csv_serializer, json_deserializer
xgboost_predictor.content_type = 'text/csv'
xgboost_predictor.serializer = csv_serializer
xgboost_predictor.deserializer = json_deserializer
result = xgboost_predictor.predict(val_df.sample(n=1))
print("prediction: {}".format(result))

45. © 2020, Amazon Web Services, Inc. or its affiliates. All rights reserved.
AmazonSageMaker -XGBoost
xgboost_predictor = xgboost.deploy(initial_instance_count=1, instance_type='ml.m5.xlarge')
from sagemaker.predictor import csv_serializer, json_deserializer
xgboost_predictor.content_type = 'text/csv'
xgboost_predictor.serializer = csv_serializer
xgboost_predictor.deserializer = json_deserializer
result = xgboost_predictor.predict(val_df.sample(n=1))
print("prediction: {}".format(result))

46. © 2020, Amazon Web Services, Inc. or its affiliates. All rights reserved.
AmazonSageMaker -XGBoost
xgboost_predictor = xgboost.deploy(initial_instance_count=1, instance_type='ml.m5.xlarge')
from sagemaker.predictor import csv_serializer, json_deserializer
xgboost_predictor.content_type = 'text/csv'
xgboost_predictor.serializer = csv_serializer
xgboost_predictor.deserializer = json_deserializer
result = xgboost_predictor.predict(val_df.sample(n=1))
print("prediction: {}".format(result))

47. © 2020, Amazon Web Services, Inc. or its affiliates. All rights reserved.
AmazonSageMaker -XGBoost
xgboost_predictor = xgboost.deploy(initial_instance_count=1, instance_type='ml.m5.xlarge')
from sagemaker.predictor import csv_serializer, json_deserializer
xgboost_predictor.content_type = 'text/csv'
xgboost_predictor.serializer = csv_serializer
xgboost_predictor.deserializer = json_deserializer
result = xgboost_predictor.predict(val_df.sample(n=1))
print("prediction: {}".format(result))

48. © 2020, Amazon Web Services, Inc. or its affiliates. All rights reserved.
AmazonSageMaker -XGBoost
xgboost_predictor = xgboost.deploy(initial_instance_count=1, instance_type='ml.m5.xlarge')
from sagemaker.predictor import csv_serializer, json_deserializer
xgboost_predictor.content_type = 'text/csv'
xgboost_predictor.serializer = csv_serializer
xgboost_predictor.deserializer = json_deserializer
result = xgboost_predictor.predict(val_df.sample(n=1))
print("prediction: {}".format(result))

49. ¡Gracias!
© 2020, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Edzon Sánchez
edzons@amazon.com