In this session, we will learn how we used Amazon Machine Learning services to enrich our datasets, we will use Amazon rekognition to extract data from pictures and Amazon comprehend to get sentiments and areas of interests from posts. We'll use Amazon SageMaker built-in algorithms to easily build and train a machine learning model and deploy it into a production-ready hosted environment.

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Yuval Fernbach
Solutions Architect, Amazon Web Services
Using AI for Real-Life Data Enrichment

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“Bringing Social Networks to Real-Life”

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Demo

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Use Case - “Ice Breaker”
• Enriching user interests from public photos
• Enriching user interests from public posts
• Predicting “IceBreaker” topics

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MakirOto Architecture

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MakirOto Architecture

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MakirOto Architecture

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MakirOto Architecture

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MakirOto Architecture

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MakirOto Architecture

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Machine Learning @ Amazon

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Fulfilment
& Logistics
At Amazon, we’ve been making investments
in ML for the last 20 years…

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At Amazon, we’ve been making investments
in ML for the last 20 years…
Fulfilment
& Logistics
Search &
Discovery

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At Amazon, we’ve been making investments
in ML for the last 20 years…
Fulfilment
& Logistics
Existing
Products
Search &
Discovery

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19. © 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved.
Fulfilment
& Logistics
Existing
Products
New
Products
Search &
Discovery
At Amazon, we’ve been making investments
in ML for the last 20 years…

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ML @ AWS: Our Mission
Put machine learning in the hands of every
developer and data scientist

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The Amazon Machine Learning Stack
PLATFORM SERVICES
FRAMEWORKS & INTERFACES
CPU instances
AWS Deep Learning AMIs
Amazon SageMaker AWS DeepLens
APPLICATION SERVICES
Amazon Rekognition
Amazon
Rekognition Video
Amazon Polly
Amazon Transcribe
Amazon Lex
Amazon Translate
Alexa for Business
Amazon Comperhend
VISION LANGUAGE
Amazon Rekognition
Amazon Comprehend
APPLICATION SERVICES
Amazon SageMaker
PLATFORM SERVICES
GPU instances ML/DL frameworks
FRAMEWORKS & INTERFACES

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Enriching User Interests From
Public Photos

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Amazon Rekognition
Object, Scene &
Activity Recognition
Facial
Recognition
Facial Analysis Person Tracking
Unsafe Content
Detection
Celebrity
Recognition
Text in Images
D e e p l e a r n i n g - b a s e d i m a g e a n d v i d e o a n a l y s i s

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Demo

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Enriching User Interests From
Public Posts

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Amazon Comprehend
Discover insights and relationships in text
Entities
Key Phrases
Language
Sentiment
Amazon
Comprehend

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Demo

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Predicting “IceBreaker” Topics

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Machine Learning Process is Complicated…
1. Data wrangling
2. Experimentation
3. Deployment
Fetch data
Clean & format
data
Prepare &
transform data
Train modelEvaluate model
Integrate with
prod
Monitor/
debug/refresh

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…and Time Consuming
Fetch data
Clean & format
data
Prepare &
transform data
Train modelEvaluate model
Integrate with
prod
Monitor/
debug/refresh
6–18
months

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Amazon SageMaker
The quickest and easiest way to get ML models from idea to production
Zero setup Flexible Model
Training
Pay by the secondEnd-to-End Machine
Learning Platform
$

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Amazon SageMaker
Fully managed
hosting with auto-
scaling
One-click
deployment
Pre-built
notebooks
for common
problems
Built-in, high
performance
algorithms
One-click
training
Hyperparameter
optimization
BUILD TRAIN DEPLOY

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Amazon SageMaker Algorithms
Amazon Built-in
Algorithms
Framework
Algorithms
Use Your Own
Algorithms

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Amazon SageMaker Algorithms
Amazon Built-in Algorithms
• Linear learner
• Factorization Machines
• XGBoost
• Image Classification
• Sequence2Sequence
• K-Means
• PCA
• LDA
• NTM
• DeepAR Forecasting
• BlazingText
Framework Algorithms
• MXNet
• TensorFlow
• Spark
Use Your Own Algorithms
• MXNet
• TensorFlow
• Spark
• Caffe
• Caffe2
• Torch
• CNTK
• …

38. © 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved.
Amazon SageMaker Algorithms
Amazon Built-in Algorithms
• Linear learner
• Factorization Machines
• XGBoost
• Image Classification
• Sequence2Sequence
• K-Means
• PCA
• LDA
• NTM
• DeepAR Forecasting
• BlazingText
Framework Algorithms
• MXNet
• TensorFlow
• Spark
Use Your Own Algorithms
• MXNet
• TensorFlow
• Spark
• Caffe
• Caffe2
• Torch
• CNTK
• …

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Demo

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Summary
• Enriching user interests from public photos
• Enriching user interests from public posts
• Predicting “IceBreaker” topics

41. © 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved.
What’s Next

42. Thank you!