Apresentação - Integrando Machine Learning - da ingestão à persistência - AWS - Amazon Web Services Big Data e Inteligência Artificial Fast Data Streaming de Dados Análise em realtime

1. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Hugo Rozestraten
Solutions Architect @ AWS
Integrando Machine Learning - da ingestão
à persistência.

2. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Machine Learning
(Aprendizagem de máquina)
“é um campo na ciência da computação que dá aos computadores
a habilidade de aprender sem serem explicitamente programados”
(Arthur Samuel - 1959)

3. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Inteligência Artificial
Aprendizado de Máquina
“ML”
DEEP
LEARNING

4. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
“Deep Learning”
Análise de Sentimento
Detecção de objetos Carro Autônomo
Reconhecimento facial
Previsão
Fala para Texto/Texto para Fala
Tradução
Jogos Video Games
Classificação de texto
Segmentação de Imagens
Classificação
Detecção de Fraude
Sistema de Recomendações

5. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Segmentação de Imagens
Source: https://github.com/msracver/FCIS

6. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Resumo – Inteligência Artifical
Source: http://www.legalexecutiveinstitute.com/artificial-intelligence-in-law-the-state-of-play-2016-part-1/

7. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Casos Práticos

8. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Carro Autônomo
Detecção de Objetos

9. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Carro Autônomo
Precisão de posicionamento em
centímetros
Segmentação por pixel em
Imagem – tempo real

10. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.© 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Person Redaction
© 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.© 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Person RedactionAnonimização de pessoas em vídeos

11. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Texto para fala

12. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Análise de Esportes

13. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Comércio sem caixas

14. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Recomendações personalizadas

15. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Detecção de doenças

16. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.

17. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Engenheiros de dados,
como conseguimos ajudar nos
objetivos de Machine Learning?

18. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Linha do tempo
DataWarehouse/BI
Real time Data
Machine Learning

19. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Dados
DataWarehouse/BI
Real time Data
Machine Learning

20. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Data Lakes estendem a abordagem
tradicional
Data warehouse
Business intelligence
OLTP ERP CRM LOB
• Dado Relacional e Não Relacional
• Escala de TBs–EBs
• Motores Analíticos Diversos
• Armazenamento de baixo custo &
Analytics
Dispositivos Web Sensores Social
Data lake
Processamento Big data,
real-time, machine learning

21. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Data Lakes na AWS
Analytics
• Durabilidade e disponibilidade na escala de EB
• Capacidade de segurança, conformidade
regulatória e auditoria
• Controle granular de acesso ao nível de objeto
• Performance mais rápida recuperando
subconjunto de dados
• Muitas maneiras de trazer os dados
• Mais integrações com parceiros
• Análise com um amplo conjunto de serviços
Machine
learning
Dados
Real-time
Dados
On-premises
Data Lake
na AWS

22. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Serviços de ML gerenciados
Deep Learning AMIs
Reconhecimento de Vídeo e Imagem
Interfaces Conversacionais
Deep-Learning Vídeo Camera
Processamento de Linguagem Natural
Tradução de línguas
Reconhecimento de voz
Text-to-Speech
Análise Interativa
Hadoop & Spark
Data Warehousing
Busca Full-text
Análise Real-time
Dashboards & Visualizações
Conexão de Rede Dedicada
Ferramentas de Segurança
Container de Embarque Reforçado
Migração de banco de dados
Dispositivos Conectados na AWS
Stream de dados Real-time
Stream de video Real-time
Data Lake
na AWS
Armazenamento | Catálogo de dados
AnalyticsMachine learning
Dados Real-timeDados On-premises
Portfolio de Data Lakes Integrado

23. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Machine Learning
DataWarehouse/BI
Real time Data
Machine Learning

24. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
CRISP-DM
• Cross Industry Standard Process for
Data Mining
• Padrão “de facto” atual para Ciência
de Dados
• Evidencia a natureza cíclica e
interativa da Ciência de Dados
https://en.wikipedia.org/wiki/Cross_Industry_Standard_Process_for_Data_Mining

25. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Vision
Aprendizado de Máquinas
Frameworks &
Infra-estrutura
GPU MobileCPU IoT (Greengrass)
Platform
Services
Application
Services
Amazon SageMaker
Rekognition
Image
Rekognition
Video
Speech
Polly Transcribe
Language
Translate ComprehendLex
TensorFlow GluonApache MXNet Cognitive
Toolkit
Caffe2 & Caffe PyTorch Keras

26. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
GPU?

27. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
GPU @AWS

28. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Notebooks - Amazon SageMaker
1 2 3 4
I I I I
Instâncias Notebook Algoritmos Treinamento Hospedagem do
Serviço

29. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
IA @AWS
Application
Services
Platform
Services
Frameworks
&
Infrastructure
Apache
MXNet
PyTorch
Cognitive
Toolkit
Keras
Caffe2
& Caffe
TensorFlow
AWS Deep Learning AMI (Ubuntu & Amazon Linux – Cuda 8 & 9)
GPU
(P2 & P3)
MobileCPU
IoT
(Greengrass)
Amazon Machine
Learning
Mechanical TurkSpark & EMR
Vision:
Rekognition
Rekognition Video
Speech:
Polly
Transcribe
Language:
Lex
Translate
Comprehend
Gluon
SageMaker
DeepLens

30. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Amazon S3 Amazon Glacier AWS Glue Data Lake
Amazon Redshift +
Spectrum
EMR AthenaKinesis Analytics
Platform
&
Frameworks
Services
Amazon
Rekognition
Lex
Polly Translate
Transcribe Comprehend
Snowball
Centro de Gravidade para Machine Learning
AWS IoT
Snowmobile
DBS Migration
AWS Greengrass
ML
Edge
MTurk
Kinesis
Amazon SageMakerGPU

31. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Machine Learning
DataWarehouse/BI
Real time Data
MachineLearning

32. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Decisões oportunas necessitam que dados estejam
disponíveis em minutos( / segundos / milisegundos )
Fonte: Perishable Insights, Mike Gualtieri, Forrester
Dados perdem valor
rapidamente ao
longo do tempo

33. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Amazon S3
Data Lake
Batch
Analytics
Stream
ing/Real-tim
e
Analytics
Amazon Kinesis
Streams & Firehose
AWS Lambda
Apache Storm
on EMR
Apache Flink
on EMR
Spark Streaming
on EMR
Hadoop / Spark
Streaming Analytics Tools
Amazon Redshift
Data Warehouse
Amazon DynamoDB
NoSQL DB & Graph
DB
Amazon
Elasticsearch
Service
Relational
DatabaseAmazon EMR
Amazon Aurora
Amazon Machine
Learning
Open Source
Tool of Choice
on EC2
DataSources
Arquitetura de
Data Lake AWS
Data Science Sandbox
Visualization /
Reporting
Amazon Kinesis
Analytics
Amazon Glue

34. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Amazon Kinesis Data Streaming
Colete, processe e analise streams de dados em tempo real
EMR/Spark
Código
customizado em
EC2
Amazon
S3
Amazon
Redshift
Splunk
Ingestão e
armazenamento
de streams de
dados
Kinesis Data
Streams
Kinesis Data
Analytics
Agregação,
filtragem e
enriquecim
ento de
dados
Kinesis Data
Firehose
Streams de
dados de
saída
AWS Lambda
• Tempo real
• Totalmente gerenciado
• Escalável
• Seguro
• Efetivo em custo
Amazon
Elasticsearch
Service

35. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Tipos de Janelas temporais
• Sliding, tumbling e custom windows
• Stagger Windows
• Tumbling windows são de tamanho fixo e as chaves agrupadas não se
sobrepõem

36. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Desafios: Escalabilidade
• Problema: Milhões de pessoas acessando/assistindo = Milhões de TPS
• Solução: Amazon Kinesis shards + processamento batching
… …
Source
Amazon Kinesis
Destination 1
Lambda
Destination 2
Shards
Lambda will scale automaticallyScale Amazon Kinesis by splitting or merging shards
Waits for responsePolls a batch

37. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Arquitetura Macro
Machine
Learning Models APIs
Sagemaker endpoint
Ads
WebSite or App
Machine Learning
Eploration
Creating Model
SageMaker

38. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Demo

39. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.

40. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.

41. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.

42. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.

43. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.

44. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.

45. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.

46. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
Obrigado